Scott A. Shappell 

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

Douglas A. Wiegmann

University of Illinois at Urbana-Champaign

Institute of Aviation

Savoy, IL 61874

March 2003

Final Report

This document is available to the public

through the National Technical Information

Service, Springfield, Virginia 22161.

Office of Aerospace Medicine

Washington, DC 20591

A Human Error Analysis of 

General Aviation Controlled 

Flight Into Terrain Accidents 

Occurring Between 1990-1998

DOT/FAA/AM-03/4
NOTICE

This document is disseminated under the sponsorship of

the U.S. Department of Transportation in the interest of

information exchange. The United States Government

assumes no liability for the contents thereof.
i

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iii

Acknowledgments

 The authors would like to express their thanks to Ms. Deborah Thompson, Mr. Dan Jack, Ms. 

Carrie Roberts, and Ms. Tara Bergsten for their tireless efforts managing and entering the data associated 

with these accidents. We also are indebted to Mr. Shawn Hoffman, Mr. Marshall Scott, Mr. Sean Sullivan, 

Mr. Robert Taylor, Mr. Joe Windes, Mr. Chris Yates, and Mr. Luis Gutierrez for their effort analyzing the 

thousands of general aviation accidents included in this study. Finally, we would be remiss if we did not 

gratefully acknowledge the editorial comments and support provided by Ms.Christy Detwiler and Mr. 

Michael Wayda during the writing and production of this manuscript. 

1

A HUMAN ERROR ANALYSIS OF GENERAL AVIATION CONTROLLED FLIGHT INTO

TERRAIN ACCIDENTS OCCURRING BETWEEN 1990-1998

INTRODUCTION

Aviation continues to be one of the safest forms of 

transportation, and with the help of modern technology, is enjoying its best years ever. Still, accidents do 

occur, leaving investigators with the unenviable and 

often difficult task of identifying the causes, in the hope 

that they might be prevented or mitigated in the future. 

Using sophisticated forensic techniques and deductive 

reasoning; the work of an accident investigator is much 

like a detective sifting through clues to solve a mystery. 

Yet, even the most skilled investigator is often at a loss 

when trying to explain how a pilot could inexplicably fly 

a functioning aircraft into the ground. These so-called 

controlled flight into terrain (CFIT) accidents continue to beleaguer both civilian and military aviation.

So, what is “controlled” flight into terrain? After all, 

it seems inconceivable that a pilot would fly an aircraft 

into the ground while it was still controllable. It should 

come as no surprise, then, that getting investigators and 

researchers to agree on what is, and more importantly, 

what is not CFIT, is difficult at best. Nevertheless, while 

individual definitions of CFIT may vary, most would 

agree at some level that CFIT occurs when an airworthy 

aircraft, under the control of a pilot, is flown into terrain 

(water or obstacles) with inadequate awareness on the 

part of the pilot of the impending disaster (FAA, 2000).

Regardless of the nuances of each investigator’s personal 

definition, no one would deny that CFIT is a serious issue 

facing aviation today. In fact, if one were to use the FAA’s 

definition (above), the U.S. Navy/Marine Corps alone 

lost an average of ten aircraft per year to CFIT between 

1983 and 1995 (Shappell & Wiegmann, 1995, 1997b). 

Likewise, between 1990 and 1999, 25% of all fatal airline accidents and 32% of worldwide airline fatalities 

(2,111 lives lost) have been attributed to CFIT (Boeing, 

2000). In fact, since 1990, no other type of accident 

has taken more lives in military or commercial aviation.

Given the accident data, no one would disagree that 

CFIT accidents in the military and commercial aviation 

warrant the attention they receive; but often forgotten 

are the even larger number of CFIT accidents that occur 

within general aviation (GA). To put it into perspective, 

while the U.S. Navy/Marine Corps lose on average 20-

30 aircraft annually for a variety of reasons, there were 

nearly 20,000 GA accidents between 1990 and 1999 

alone — including an average of almost 400 fatal accidents per year (NTSB, 2001). Unfortunately, neither 

the National Transportation Safety Board (NTSB), nor 

anyone else that we are aware of, has documented the 

number of CFIT accidents occurring in GA annually. 

But even if only 10% of the fatal GA accidents involved 

CFIT (well below the averages reported in commercial or 

military aviation), an alarming 40 fatal accidents per year 

could be attributed to this seemingly purest of human 

error accidents – and this does not even take into account 

those CFIT accidents in which a fatality did not occur.

CFIT Joint Safety Analysis Team (JSAT)

On April 14, 1998, the FAA Administrator outlined the 

Agency’s safety agenda for GA, commercial aviation, and 

cabin safety. Referred to as Safer Skies, the goal for GA was 

to significantly reduce fatal accidents over a 11-year period 

from 1996 to 2007. To accomplish that goal, six focus 

areas were targeted, one of which was CFIT. Armed with 

this mandate, a unique team of industry and FAA safety 

professionals, the Joint Safety Analysis Team (JSAT), was 

formed in the fall of 1998 to “identify and implement a 

data driven, cost/benefit focused, safety enhancement program designed to reduce fatal general aviation accidents” 

(FAA, 2000, p.13) – in particular, those involving CFIT.

The team, using accidents identified by a previous 

study of CFIT performed by the Volpe Center (Volpe, 

1997), examined 195 CFIT accidents that occurred 

between 1993 and 1994 under a variety of operations 

including: 14 CFR Part 91 (personal and business flying), 

14 CFR Part 125 (privately operated transport aircraft), 

14 CFR Part 133 (rotary wing external operations), 14 

CFR Part 135 (air taxi), and 14 CFR Part 137 (agricultural aerial application) operations. Employing a root 

cause analysis approach, the CFIT JSAT conducted a 

detailed analysis of the CFIT accidents and identified 

55 interventions aimed at addressing their causes. Ultimately, the team selected a set of ten interventions that 

would be the most effective and feasible to implement. 

In no particular order they included the following:

• Increase pilot awareness of accident causes.

• Improve the safety culture within the aviation community.

• Promote the development and use of low-cost terrain 

clearance and/or look ahead devices.
2 

• Improve pilot training (i.e., weather briefing, equipment, decision-making, wire and tower avoidance, and 

human factors).

• Improve the quality and substance of weather briefs.

• Enhance the Biennial Flight Review (BFR) and/or 

instrument competency check.

• Develop and distribute mountain flying technique 

advisory material.

• Standardize and expand the use of markings for towers 

and wires.

• Use high-visibility paint and other visibility enhancing 

features on obstructions.

• Eliminate the pressure to complete the flight where 

continuing may compromise safety.

Even the best interventions are useless if a plan for implementing them is not drawn up. With that in mind, the 

FAA chartered a second team, including several members 

of the original CFIT JSAT, to develop an implementation 

plan for incorporating the recommendations of the CFIT 

JSAT into practice. The CFIT Joint Safety Implementation Team (JSIT) subsequently released a detailed implementation plan around eight areas that were in line with 

the original CFIT JSAT report (FAA, 2000). Included in 

their plan was an implementation strategy, the identification of responsible parties and resources, and a list of 

milestones/completions dates to monitor the program.

U.S. Navy/Marine Corps CFIT

The military, like their civilian counterparts, has been 

confronted with CFIT almost since the inception of military aviation. Nevertheless, few studies have systematically 

examined the full spectrum of human error associated 

with these often fatal accidents. Shappell and Wiegmann 

(1997a) did conduct one such study, examining 144 U.S. 

Navy/Marine Corps Class A1 accidents using an early 

version of the Human Factors Analysis and Classification 

System (Shappell & Wiegmann, 1997b). Their analyses 

revealed several findings applicable to the intervention of 

CFIT, some of which were unexpected, given conventional 

wisdom in the area. What was consistent with previous 

work, however, was that many of the U.S. Navy/Marine 

Corps CFIT accidents were associated with spatial disorientation and adverse mental states such as fatigue and 

the loss of situational awareness. In fact, to the extent 

that any particular causal category can be considered 

characteristic of a particular type of accident, it would 

be adverse mental and physiological states with CFIT.

While the confirmation that spatial disorientation and 

adverse mental states contribute to CFIT was important, 

what was particularly revealing from the Navy study was 

the large number of CFIT accidents associated with the 

willful violation of the rules by aircrew — a surprising 40% 

of the CFIT accidents examined. Upon further review, 

it appears that whether the violations involved personal 

readiness (e.g., self-medicating or simply violating crew 

rest requirements) or unsafe act violations (e.g., flying into 

a cloud bank when authorized for visual flight rules only), 

they were often the seminal event in the tragic sequence of 

events that followed. This finding was particularly relevant 

because many of the interventions proposed to prevent 

CFIT involved terrain avoidance and ground proximity 

warning systems (GPWS) that would seemingly be of little 

help if aircrew were willing to violate established safety 

practices. In fact, it was felt that over-reliance on GPWS 

and other related terrain avoidance systems might actually increase the likelihood that aircrew will push altitude 

limits in an attempt to get an edge in training or combat.

Even more interesting than the relationship of violations with CFIT were the marked differences between 

the error patterns associated with CFIT occurring 

during the day versus night. Much to the surprise of 

many within Naval aviation, nearly half of all CFIT 

accidents occurred in broad daylight during visual meteorological conditions (VMC). After all, it had been 

generally thought that most, if not all, CFIT occurred 

during the night or when visual cues were otherwise 

impoverished during instrument meteorological conditions (IMC). It seemed reasonable therefore to ask 

whether any additional differences existed between day 

and night CFIT other than the obvious visual ones.

It is well known that when visual cues are limited, 

coordination among the crew and personnel external to 

the cockpit becomes paramount. It is not surprising then 

that the incidence of crew resource management failures 

was significantly higher among U.S. Navy/Marine Corps 

crews during night than during daytime CFIT accidents. 

Likewise, adverse physiological (e.g., spatial disorientation) and mental states (e.g., loss of situational awareness) 

were more prevalent at night than during the day. This 

was also anticipated, given that the lack of visual cues 

would presumably lead to spatial disorientation and the 

1

 The U.S. Navy/Marine Corps considers an accident as Class A if the total cost of property damage (including all aircraft damage) is 

$1,000,000 or greater; or a naval aircraft is destroyed or missing; or any fatality or permanent total disability occurs with direct involvement 

of naval aircraft.
3

loss of situational awareness. What was not expected was 

the rather large proportion of violations (nearly half of all 

the CFIT accidents examined) occurred mostly during the 

day. Given that violations almost invariably predicate other 

factors within the HFACS framework, this finding became 

a significant source of information for those designing 

systems to address CFIT in the U.S. Navy/Marine Corps.

A rationale for an HFACS analysis of GA accidents

Without question, the work of the CFIT JSAT and 

JSIT represent a landmark effort within civil aviation. 

However, while the interventions identified by the CFIT 

JSAT represent the views and opinions of experts in industry and the FAA, their findings might have benefited 

from a more focused human error analysis like that used 

with the U.S. Navy/Marine Corps accidents – particularly 

one that was not constrained by a relatively small sample 

of accidents. This is not to imply that the CFIT JSAT 

study was flawed. Quite the contrary, the CFIT JSAT 

was working within the logistical and time constraints 

they were given. As a result, they based their conclusions 

on a relatively small subset of accidents from a variety of 

aircraft operations rather than GA alone. This was done 

primarily because no one had systematically examined the 

GA accident record for CFIT accidents, perhaps due to 

the general lack of agreement on what a CFIT accident 

is. Since the JSAT convened, however, a joint International Civil Aviation Organization (ICAO)/Commercial 

Aviation Safety Team (CAST) Common Taxonomy Team 

has published a definition of CFIT accepted by many in 

the field (including the National Transportation Safety 

Board in the United States and ICAO) similar to that 

used by the CFIT JSAT. Specifically, the ICAO/CAST 

defined CFIT as an “inflight collision with terrain, water, or obstacle without indication of a loss of control.”

The aim of this study therefore was to examine a 

large body of GA accidents using the CAST/ICAO 

criteria for CFIT. Then, after differentiating CFIT 

from non-CFIT accidents, a more detailed human error 

analysis could be performed. Given the success that the 

U.S. Navy/Marine Corps and other organizations have 

experienced using HFACS, it seemed reasonable to apply the HFACS framework to the GA accident database 

in the hope that similar results could be achieved. To 

familiarize the reader with the relevant aspects of the 

HFACS framework, it will be briefly reviewed here. 

Note however that a more complete description can 

be found elsewhere (Shappell & Wiegmann, 2001a).

HFACS

It is generally accepted that aviation mishaps, like most 

accidents, do not happen in isolation. Rather, they are the 

result of a chain of events often culminating in the unsafe 

acts of aircrew. From Heinrich’s (Heinrich, Peterson, & 

Roos, 1931) axioms of industrial safety to Bird’s (1974) 

“Domino theory,” a sequential theory of accident causation has been embraced by many in the field. Particularly 

useful in this regard has been Reason’s (1990) relatively 

recent description of active and latent failures within 

the context of his “Swiss cheese” model of human error.

In general, Reason described four levels of human 

failure (organizational influences, unsafe supervision, 

preconditions for unsafe acts, and the unsafe acts of 

operators), each one affecting the next. To hear Reason 

explain it, many accidents have their roots high within 

the organization, at the level of the chief executive officer, president and vice-president(s). It is the decisions 

made by those at the top that often influence the middle 

managers and supervisors as they oversee the day-to-day 

operations of the organization. Ultimately, it is the operators at the “pointy end of the spear” who inherit all 

the baggage of the organization along with those that 

manage them as they perform their duties. Unfortunately, 

when the system breaks down and errors occur, accidents 

and incidents are the end result. So, if one wants to truly 

understand the causal genesis of an accident, they must 

peel the proverbial onion back, layer-by-layer, until the 

causal sequence of events is uncovered in its entirety. 

Yet, even as Reason’s seminal work revolutionized the 

way we in aviation and other industrial settings view the human causes of accidents, it did not provide the level of detail 

necessary to apply it in the real world. Therefore, drawing 

upon Reason’s (1990) original work, the human factors 

analysis and classification system (HFACS) was developed 

to fill that need (Shappell & Wiegmann, 2000a, 2001a).

The HFACS framework describes 17 causal categories within Reason’s four levels of human failure (Figure

1). However, because our previous work (Shappell & 

Wiegmann, 2001b) using GA accidents has shown that 

the causal factors typically populate only the bottom two 

tiers of HFACS (the preconditions for unsafe acts and the 

unsafe acts of operators) we will limit our discussion to 

them. A complete description of all four tiers can be 

found elsewhere (Shappell & Wiegmann, 2000a, 2001a).

Unsafe Acts of Operators

The first level of HFACS describes those unsafe acts of 

operators that can lead to an accident. Perhaps unfairly 

referred to in aviation as aircrew/pilot error since many 
4 

unsafe acts do not involve aircrew, this level is where 

most accident investigations are focused and consequently, 

where the majority of causal factors are uncovered. The 

unsafe acts of operators can be loosely classified into one 

of two categories: errors and violations. While both are 

common within most settings, they differ markedly when 

the rules and regulations of an organization are considered. 

That is, errors can best be described as those activities that 

fail to achieve their intended outcome, while violations are 

commonly defined as behavior that represents the willful 

disregard for the rules and regulations. However, merely 

distinguishing between errors and violations does not provide the level of granularity required of most error analyses 

and accident investigations. Therefore, the categories of 

errors and violations were expanded here (Figure 1), as 

elsewhere (Reason, 1990; Rasmussen, 1982), to include 

three basic error types (decision, skill-based, and perceptual) and two forms of violations (routine and exceptional).

Errors

Decision Errors. Perhaps the most heavily investigated 

of all error forms, decision errors represent intentional 

behavior that proceeds as intended, yet the plan proves 

inadequate or inappropriate for the situation. Often referred to as “honest mistakes,” this type of error can generally be grouped into one of three categories: procedural 

errors, poor choices, and problem-solving errors (Table

1). Procedural decision errors (Orasanu, 1993), or rulebased mistakes (as described by Rasmussen, 1982), occur 

during highly structured tasks of the sorts, if A, then do B, 

then do C. Aviation, particularly within the military and 

commercial sectors, by its very nature is highly structured, 

and consequently, much of pilot decision-making is procedural. In fact, there are very explicit procedures to be 

performed in virtually all phases of flight. Still, errors can, 

and often do, occur when a situation is either not recognized or misdiagnosed and the wrong procedure is applied.

Even in aviation, however, not all situations have 

corresponding procedures that address them. Instead, 

many situations require that a choice be made among 

multiple response options. Consider, for instance, the 

pilot who unexpectedly confronts a line of thunderstorms directly along the intended flight path. He or 

she can choose to fly around the weather, divert to 

another field until the weather passes, or penetrate the 

weather hoping to quickly transition through it. When 

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confronted with situations such as these, choice decision 

errors (Orasanu, 1993), or knowledge-based mistakes 

as they are otherwise known (Rasmussen, 1982), may 

occur. This is particularly true when there is insufficient experience, time, or other outside pressures that 

may preclude correct decisions. Put simply, sometimes 

individuals chose well, and sometimes they don’t.

Finally, there are occasions when a problem is not well 

understood and formal procedures or response options 

are not available. It is during these ill-defined situations 

that the construction of a novel solution is required. In a 

sense, individuals find themselves where no one has been 

before, and in many ways, must “fly by the seats of their 

pants.” Individuals placed in this situation must resort 

to slow and effortful reasoning processes where time is a 

luxury rarely afforded. Consequently, while this type of 

decision-making is more infrequent than other types, the 

relative proportion of errors committed is markedly higher.

Skill-based Errors. In contrast to decision errors, the 

second error form, skill-based errors, occur with little or no 

conscious thought. Just as little thought goes into turning 

one’s steering wheel or shifting gears in an automobile, 

basic flight skills such as stick and rudder movements and 

visual scanning often occur without conscious thought. 

The difficulty with these seemingly automatic behaviors 

is that they are particularly susceptible to attention and/or 

memory failures. In fact, attention failures have been 

linked to many skill-based errors such as the breakdown 

in visual scan patterns, task fixation, and the inadvertent 

activation of controls. Consider, for example, a crew that 

becomes so fixated on trouble-shooting a burned out 

warning light that they fail to monitor their altimeter 

and end up flying into the ground. Perhaps a bit closer 

to home, consider the hapless soul who locks himself out 

of the car or misses his exit while driving because he was 

either distracted, in a hurry, or daydreaming. These are 

both examples of attention failures that commonly occur during highly automatized behavior. While at home 

or driving around town, these attention failures may 

be frustrating, but in the air they can be catastrophic.

In contrast, memory failures often appear as omitted 

items in a checklist, place losing, or forgotten intentions. 

For example, many of us have forgotten to replace the 

gas cap after refueling the family car or failed to put the 

coffee in the coffeepot before turning it on. Likewise, it is 

not difficult to imagine that when under the stress of an 

inflight emergency, for example, or after a long, fatiguing 

flight, critical steps in a procedure can be missed. Yet, even 

when not particularly stressed, individuals have forgotten 

to set the flaps on approach or lower the landing gear.

Even the manner (or skill) with which one flies an 

aircraft (aggressive, tentative, or controlled) can affect 

safety. For example, two pilots with identical training, 

flight grades, and experience may differ significantly in 

the way they fly. That is, some pilots may fly smooth and 

effortlessly, while others are more forceful and rough on 

the flight controls. Both may be safe and equally proficient 

in the air; however, given certain scenarios, the techniques 

they employ could set them up for failure. Likewise, there 

are some pilots who are very safe in daytime VMC conditions, but put them in a situation where they are flying at 

night or IMC and their skill quickly degrades to unsafe 

levels. In the end, such techniques are as much a factor 

of innate ability and aptitude as they are an overt expression of one’s personality, making efforts at the prevention 

and mitigation of technique errors particularly difficult.

Perceptual Errors. While, decision and skill-based 

errors have dominated most accident databases and 

have therefore been included in most error frameworks, 

perceptual errors have received comparatively less attention. No less important, perceptual errors occur when 

sensory input is degraded or “unusual,” as is often the 

case when flying at night, in the weather, or in other visually impoverished environments. Faced with acting on 

imperfect information, aircrew run the risk of misjudging 

distances, altitude, and decent rates, as well as a responding incorrectly to a variety of visual/vestibular illusions.

It is important to note, however, that it is not the illusion or disorientation that is classified as a perceptual 

error. Rather, it is the pilot’s erroneous response to the illusion or disorientation that is captured here. For example, 

many pilots have experienced spatial disorientation (often 

referred to as the “leans”) when flying in IMC. In instances 

such as these, pilots are taught to rely on their primary 

instruments, rather than their senses when controlling the 

aircraft. Still, some pilots fail to monitor their instruments 

when flying in adverse weather or at night, choosing instead to fly using fallible cues from their senses. Tragically, 

many of these aircrew and others who have been fooled 

by illusions and other disorientating flight regimes have 

wound up on the wrong end of the accident investigation.

Violations

By definition, errors occur while aircrews are behaving within the rules and regulations implemented 

by an organization and typically dominate most accident databases. In contrast, violations represent 

the willful disregard for the rules and regulations 

that govern safe flight and, fortunately, occur much 

less frequently (Shappell & Wiegmann, 1995).

Routine Violations. While there are many ways to 

distinguish between types of violations, two distinct forms 

have been identified, based on their etiology. The first, routine violations,tend to be habitual by nature and are often 

tolerated by the governing authority (Reason, 1990). Consider, for example, the individual who drives consistently 
7

5-10 mph faster than allowed by law or someone who 

routinely flies in marginal weather when authorized for visual meteorological conditions only. While both certainly 

violate governing regulations, many drivers or pilots do the 

same thing. Furthermore, people who regularly drive 64 

mph in a 55-mph zone, almost always drive 64 in a 55-

mph zone. That is, they routinely violate the speed limit.

Often referred to as “bending the rules,” these violations are often tolerated and, in effect, sanctioned by 

authority (i.e., you’re not likely to get a traffic citation 

until you exceed the posted speed limit by more than 

10 mph). If, however, local authorities started handing out traffic citations for exceeding the speed limit 

on the highway by 9 mph or less, then it is less likely 

that individuals would violate the rules. By definition 

then, if a routine violation is identified, investigators 

must look further up the causal chain to identify those 

individuals in authority who are not enforcing the rules.

Exceptional Violations. In contrast, exceptional violations appear as isolated departures from authority, not 

necessarily characteristic of an individual’s behavior nor 

condoned by management (Reason, 1990). For example, 

an isolated instance of driving 105 mph in a 55-mph zone 

is considered an exceptional violation. Likewise, flying 

under a bridge or engaging in other particularly dangerous 

and prohibited maneuvers would constitute an exceptional violation. However, it is important to note that, 

while most exceptional violations are indefensible, they 

are not considered exceptional because of their extreme 

nature. Rather, they are considered exceptional because 

they are neither typical of the individual nor condoned 

by authority. Unfortunately, the unexpected nature of 

exceptional violations make them particularly difficult to 

predict and problematic for organizations to deal with.

Preconditions for Unsafe Acts

Simply focusing on unsafe acts, however, is like focusing on a patient’s symptoms without understanding the 

underlying disease state that caused them. As such, what 

makes Reason’s (1990) “Swiss cheese” model particularly 

useful in accident investigation, is that it encourages investigators to address the latent failures within the causal 

sequence of events as well as the more obvious, active 

failures described above. As their name suggests, latent 

failures, unlike their active counterparts, may lie dormant 

or undetected for hours, days, weeks, or even longer, until 

one day they adversely affect the unsuspecting aircrew. 

Historically, such latent failures have often been overlooked by investigators, largely because the so-called “holes 

in the cheese” that adversely affect aircrew performance 

have not been clearly defined. To remedy this, HFACS 

describes two major subdivisions within the preconditions for unsafe acts: Substandard conditions of operators 

and the substandard practices they commit (Figure 1).

Substandard Conditions of the Operators

Adverse Mental States. Being prepared mentally 

is critical in nearly every endeavor, perhaps more so 

in aviation. With this in mind, the first of three categories, adverse mental states, was created to account 

for those mental conditions that adversely affect performance (Table 2). Principal among these are the loss 

of situational awareness, mental fatigue, and pernicious 

attitudes like overconfidence and complacency, which 

negatively affect decisions and contribute to unsafe acts.

Consider, for example, the individual who is mentally fatigued or suffering the effects of sleep loss. The 

likelihood that an error will occur given these preconditions becomes more predicable. In a similar manner, 

overconfidence and other pernicious attitudes such 

as arrogance and impulsivity influence the likelihood 

that a violation will be committed. Clearly then, any 

framework of human error must account for these 

preexisting adverse mental states if a thorough understanding of the causal chain of events is to be realized.

Adverse Physiological States. The second category, adverse physiological states, refers to those 

medical or physiological conditions that interfere 

with safe operations (Table 2). Particularly important 

to aviation are such conditions as visual illusions and 

spatial disorientation as described earlier, as well as 

physical fatigue and the myriad of pharmacological and 

medical abnormalities known to affect performance.

While the adverse effects associated with visual illusions and spatial disorientation are well known among 

those in aviation circles, the effects of simply being ill 

on aircrew performance are less well known and often 

overlooked. Consider the pilot suffering from the common head cold. Unfortunately, most aviators view a 

head cold as only a minor inconvenience that can be 

easily remedied using over-the-counter antihistamines, 

acetaminophen, and other non-prescription medications. 

However, it is not the overt symptoms of the cold that 

flight surgeons are concerned with. Rather, it is the accompanying ear infection and the increased likelihood 

of spatial disorientation when entering IMC that is 

alarming — not to mention the side-effects of antihistamines, fatigue, and sleep loss on pilot decision-making.

Physical/Mental Limitations. The final class of substandard conditions involves individual physical/mental 

limitations (Table 2). Specifically, this category refers to 

those instances when mission requirements exceed the 

capabilities of the individual at the controls. For example, the human visual system is severely limited at 
8 

night; yet, when driving an automobile, many drivers 

do not necessarily slow down or take additional precautions. Likewise, in aviation, while slowing down is 

not necessarily an option, increasing one’s vigilance for 

other aircraft or obstacles whose size or contrast interferes 

with their detection will often increase the safety margin.

Similarly, there are occasions when the time required 

to complete a task or maneuver exceeds an individual’s 

capacity. That is, while good pilots are typically noted 

for their ability to react quickly and accurately, individuals vary widely in their ability to process and respond 

to information. Still, even given individual differences, if any operator or pilot is required to respond 

quickly (as is the case in many aviation emergencies), 

the probability of making an error will likely increase.

In addition to the basic sensory and information processing limitations described above, there are at least two 

additional instances of physical/mental limitations that 

need to be addressed, albeit often overlooked by most 

safety professionals. These limitations involve individuals 

who simply are not compatible with aviation, because 

they are either physically unsuited or do not possess the 

aptitude to fly. For example, some individuals simply do 

not have the physical strength or dexterity to operate in 

the unique aviation environment, or for anthropometric 

reasons, simply have difficulty reaching the controls. In 

other words, cockpits have traditionally not been designed with all shapes, sizes, and physical abilities in mind.

Likewise, not everyone has the mental ability or 

aptitude for flying aircraft. Just as not all of us can be 

concert pianists or NFL linebackers, not everyone has 

the innate ability to pilot an aircraft – a vocation that 

requires the unique ability to make decisions quickly 

and respond accurately in life-threatening situations. 

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9

The difficult task for the safety professional is identifying whether physical abilities or aptitude might 

have contributed to the accident causal sequence.

Substandard Practices of the Operator

Clearly, then, numerous substandard conditions of 

operators can, and do, lead to the commission of unsafe 

acts. Nevertheless, there are a number of things that individuals do to themselves that set up these substandard 

conditions. Generally speaking, the substandard practices of operators can be summed up in two categories: 

crew resource management and personal readiness.

Crew Resource Management. Good communication skills and team coordination have been the mantra 

of industrial/organizational and personnel psychologists for decades. As one might expect, crew resource 

management has been a cornerstone of many aviation 

safety programs as well (Helmreich & Foushee, 1993; 

Wiegmann & Shappell, 1999). As a result, the category 

of crew resource mismanagement was created to account for occurrences of poor coordination among 

personnel. Within the context of aviation, this includes 

coordination both within and between aircraft, with 

air traffic control personnel and maintenance control, 

as well as with facility and other support personnel as 

necessary. Likewise, good crew resource management 

includes coordination before and after the flight in the 

form of pre-flight briefings and debriefings as necessary.

Personnel Readiness. In aviation, or for that matter 

in any occupational setting, individuals are expected to 

show up for work ready to perform at optimal levels. 

However, in aviation as in other professions, individuals 

have been known to report for duty ill prepared, having 

violated crew rest requirements, bottle-to-brief rules, 

and rules associated with self-medicating. For example, 

when individuals violate crew rest requirements, they 

run the risk of mental fatigue and other adverse mental 

states that may ultimately lead to errors and accidents2. 

Still, not all personal readiness failures occur because of 

violations of governing rules or regulations. For instance, 

running 10 miles before piloting an aircraft may not be 

against any existing regulations, yet it may impair the 

physical and mental capabilities of the individual enough 

to degrade performance and elicit unsafe acts. Likewise, the 

traditional “candy bar and coke” lunch of the modern businessman might be common but may not be sufficient to 

sustain performance in the often complex and demanding 

environment of aviation. While there may be no rules 

governing such behavior, pilots must use good judgment 

when deciding whether they are ready and “fit” to fly.

METHOD

Data

General aviation accident data from calendar years 

1990-98 was obtained from databases maintained by 

the NTSB and the FAA’s National Aviation Safety Data 

Analysis Center (NASDAC). In total, 17,994 GA accidents were extracted for analysis. These so-called “GA” 

accidents actually included a variety of aircraft being flown 

under several different operating rules: 1) 14 CFR Part 

91 – Civil aircraft other than moored balloons, kites, 

unmanned rockets, and unmanned free balloons; 2) 14 

CFR Part 91F – Large and turbine-powered multiengine 

airplanes; 3) 14 CFR Part 103 – Ultralight vehicles; 4) 

14 CFR Part 125 – Airplanes with seating capacity of 20 

or more passengers or a maximum payload capacity of 

6,000 pounds or more; 5) 14 CFR Part 133 – Rotorcraft 

external-load operations; 6) 14 CFR Part 137 – Agricultural aircraft operations. In addition, the database 

contained several accidents involving public use aircraft 

(i.e., law enforcement, state owned aircraft, etc.) and 

some midair accidents involving military aircraft. The 

distribution of each of these accident categories within 

the NTSB/NASDC databases is presented in Table 3.

Of the 17,994 accidents listed in Table 3, 157 investigations still remained incomplete at the time of this 

analysis and were eliminated from further consideration3. 

An additional 1,168 accidents were classified as due to 

undetermined causes and were also eliminated from the 

analysis. In addition, we were concerned with the apparent 

heterogeneity of the accident sample as depicted in Table

3 even though all of the accidents listed can be found 

within the NTSB under the heading of “general aviation.” 

However, we were only interested in those accidents involving aircraft operating under 14 CFR Part 91. After 

all, it is difficult to envision that large commercial aircraft 

being ferried from one airport to the next (operating 

under 14 CFR Part 91F) or aircraft being used to spread 

chemicals on a field (operating under 14 CFR Part 137) 

can be equated with small private aircraft being flown 

for personal or recreational purposes (operating under 

14 CFR Part 91). This left us with 16,510 accidents 

in the database. Next, the accidents were examined for 

2

Note that violations that affect personal readiness are not considered “unsafe act, violations” since they typically do not happen in the cockpit, 

nor are they necessarily active failures with direct and immediate consequences.

3

 The NTSB classifies the results of accident investigations as either “preliminary” or “final” within their database. Only those designated as 

final by the NTSB as of May 30, 2002 were used in this study.
10 

aircrew-related causal factors. Again, we were only interested in those involving aircrew error, not those accidents 

that were purely mechanical in nature or those with other 

human involvement. This does not mean that mechanical 

failures or other sources of human error did not exist in 

the final database, only that some form of aircrew error 

was also involved in each of the accidents included in the 

final database. In the end, 14,086 accidents involving 

31,491 aircrew causal factors were included and submitted to further analyses using the HFACS framework.

Causal Factor Classification using HFACS

Five GA pilots were recruited from the Oklahoma 

City area as subject matter experts and received roughly 

16 hours of training on the HFACS framework. All five 

were certified flight instructors with a minimum of 1,000 

flight hours in GA aircraft (mean = 3,530 flight hours) as 

of June 1999 when the study began. After training, the 

five GA pilot-raters were randomly assigned accidents so 

at least two separate pilot-raters analyzed each accident 

independently. Using narrative and tabular data obtained 

from both the NTSB and the FAA NASDAC, the pilot-raters were instructed to classify each human causal 

factor using the HFACS framework. Note, however, 

that only those causal factors identified by the NTSB 

were classified. That is, the pilot-raters were instructed 

not to introduce additional casual factors that were not 

identified by the original investigation. To do so would 

be presumptuous and only infuse additional opinion, 

conjecture, and guesswork into the analysis process.

 After our pilot-raters made their initial classifications of the human causal factors (i.e., skill-based error, decision-error, etc.), the two independent ratings 

were compared. Where disagreements existed, the 

corresponding pilot-raters were called into the laboratory to reconcile their differences and the consensus 

classification was included in the database for further 

analysis. Overall, pilot-raters agreed on the classification of causal factors within the HFACS framework 

more than 85% of the time (29,534 agreements; 4519 

disagreements), an excellent level of agreement considering that this was, in effect, a decision-making task.4

CFIT analysis

In addition to the analysis of human causal factors 

using HFACS, the five pilot-raters were instructed to 

independently classify each accident as CFIT or nonCFIT using the definition provided by the ICAO/CAST 

Common Taxonomy Team that defined CFIT as the “inflight collision with terrain, water, or obstacle without 

indication of a loss of control.” Accompanying the 

definition were a series of usage notes that further defined the accident category. They included the following:

• CFIT is used only for accidents occurring during 

airborne phases of flight.

• CFIT includes collisions with those objects extending 

above the surface (for example: towers).

• CFIT can occur during either Instrument Meteorological Conditions (IMC) or Visual Meteorological 

Conditions (VMC).

• This category includes instances when the aircrew is 

affected by visual illusions (e.g., black hole approaches) 

that result in the aircraft being flown under control 

into terrain, water, or obstacles.

• If control of the aircraft is lost (induced by crew, weather, 

or equipment failure), do not use this category.

• Do not use this category for occurrences involving 

intentional flight into terrain (i.e., suicide).

• Do not use this category for occurrences involving 

runway undershoot/overshoot.

Finally, there was some concern that intrinsic 

differences between controlled flight into “terrain” 

(water or the ground) and controlled flight into 

“obstacles” (e.g., telephone wires, buildings, or other 

man-made structures) might exist. For this reason, 

pilot-raters were also instructed to differentiate 

CFIT accidents along this dimension as well.

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4

 The measure of agreement was a combined analysis of all accidents coded under the NTSB classification of “general aviation” and therefore 

includes accidents other than 14 CFR Part 91 as described above. A breakout by 14 CFR Part 91 alone was not possible at this time but there 

is no reason to believe that the level of agreement would change appreciably.
11

RESULTS

The GA data were initially examined to determine 

the extent to which each HFACS causal category contributed to GA accidents overall. To accomplish this, the 

frequency and percentage of GA accidents associated with 

each HFACS causal category were calculated. However, 

to avoid over-representation by any single accident, each 

causal category was counted a maximum of one time per 

accident. In this way, the count acted as an indicator of 

the presence or absence of a particular HFACS causal category for a given accident. The data were calculated in this 

manner with the knowledge that most aviation accidents 

are associated with multiple causal factors, including on 

some occasions, multiple instances of the same HFACS 

causal category (e.g., multiple decision errors may have 

been committed). However, only by analyzing the data in 

this way could a true representation of the percentage of 

accidents associated with each causal category be obtained.

The number and percentage of accidents associated 

with at least one instance of a particular HFACS causal 

category can be found in Figure 2, with one notable exception. As with post-hoc data examined in other venues 

(e.g., the U.S. Navy/Marine Corps, U.S. Army, U.S. Air 

Force, etc.), it proved too difficult to differentiate between 

routine and exceptional violations using narrative data 

from the NTSB and NASDAC. As a result, pilot-raters 

were instructed to use the parent causal category of “violations,” rather than distinguish between the two types.

The overall analysis of 14 CFR Part 91 accidents revealed a picture of human error within GA that was not 

possible before the development of HFACS. For instance, 

the data indicate that skill-based errors (73.5% of the 

14,086 GA accidents) were the most frequently cited 

unsafe act committed by aircrew, followed by decision 

errors (35.1%), violations (14.3%), and perceptual errors 

(7.7%). The finding that the unsafe acts of operators accounted for the majority of causal factors in the database 

was anticipated, given the emphasis of most investigations. However, the preconditions for unsafe acts were 

no less important. In fact, physical/mental limitations 

were among the most prevalent of all the HFACS causal 

categories cited, contributing to 18.3% of the accidents 

examined. The remaining preconditions for unsafe acts, 

in order of prevalence, were CRM failures (10.6%), 

adverse mental states (5.3%), adverse physiological 

states (2.6%), and personal readiness failures (2.1%).

The preceding analysis of the data represents a “quick 

look” at the human error issues facing GA. Yet, alone it 

provides little insight into the pattern of errors associated 

with any specific type of accident, like CFIT. The next 

step, therefore was to investigate what differences, if any, 

existed in the type and frequency of errors committed by 

aircrew involved in CFIT versus those observed in other 

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12 

types of accidents. An examination of the GA accidents 

revealed that 1,407 (roughly 10 percent), of the 14,086 

accidents were classified as CFIT by our pilot-raters using 

the criteria established by the CAST/ICAO Common 

Taxonomy Team. While the actual number and percentage 

of accidents associated with CFIT is a new and important 

finding in and of itself, the larger question was whether 

there were any differences in the pattern of errors associated with CFIT and the 12,679 non-CFIT accidents.

An inspection of Figure 3 reveals that the proportion of accidents associated with each HFACS causal 

category varied markedly between CFIT and non-CFIT 

accidents. The difficulty was in determining which differences, if any, were actually significant, and more 

importantly, which were meaningful. Traditionally, 

nonparametric statistics, like Chi-square, are used to 

measure the association between two nominal (indicator) 

variables. However, Chi-square, like many other nonparametric statistics, are fraught with problems where 

large data sets are involved. That is, as the sample size 

increases, the more likely it is to find significance where 

only small, perhaps trivial, differences actually exist.

One option is to use a measure of association that is not 

affected by sample size, like the odds ratio. Commonly 

used in epidemiology, the odds ratio is typically used to 

measure the degree of the association between two variables or the ratio of the odds of suffering some particular 

fate given certain characteristics. Consider, for example, 

the odds of surviving an automobile accident with or 

without using a seatbelt5. If drivers suffer fatal injuries 

20% of the time when they use their seatbelts, the odds 

of dying in a car accident while wearing a seatbelt are 

0.25 (0.2 die with their seatbelt on / 0.8 survive with 

their seatbelt on). In contrast, 35% of drivers not wearing seatbelts die in automobile accidents, giving odds of 

0.538 (0.35 die with their seatbelt off / 0.65 live with 

their seatbelt off). Thus, the odds ratio is 0.465 (0.25/

0.538). In other words, you have a 0.465 times higher 

chance of dying in an automobile accident with your 

seatbelt on than without it. Arguably, this is hard to 

interpret, so with numbers of less than one we typically 

calculate the inverse of the odds ratio, which in this case 

equals 2.15 (1/0.465). This means that you would be 

2.15 times more likely to die in an automobile accident 

if you did not wear your seatbelt than if you had worn it.

Another option is to dispense with traditional nonparametric statistics altogether, and compare the differences observed in the percentage data associated with 

5 These data are hypothetical and for illustrative purposes only. They are not the official statistics of the NTSB or Bureau of Transportation 

Statistics.

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13

each HFACS causal category for CFIT and non-CFIT 

accidents against some preset level considered “operationally relevant.” But, who is to say which differences are 

operationally relevant, and which are not? After all, is a 

difference between CFIT and non-CFIT accidents of 

five percentage points more operationally relevant than 

say three or four percent — or perhaps, one should use a 

larger percentage like 10 percent? In the end, the decision 

is subjective and often left to the researcher to defend.

Regardless of whether one uses traditional statistics or 

simply chooses an operationally relevant difference, there 

really is no right or wrong answer. Therefore, left without 

a clear-cut option, we chose to use the more objective approach of nonparametric statistics (Chi square and odds 

ratios) but with a considerably more conservative p value 

(p<.001) than is typically reported in other studies (p<.05 

is generally regarded as acceptable within the psychological 

literature). Our intention was to capitalize on the objective power of statistics while minimizing the problems 

associated with potentially inconsequential findings.

Using this approach, the results of the Chi-square analysis are presented for each HFACS causal category in Table 4. 

Also included are the corresponding odds ratios with a 95% 

confidence interval as a measure of the relative risk of CFIT 

given a particular causal category. For illustrative purposes, 

the results of the analyses in Table 4 have been translated 

into Figure 3 by shading the corresponding HFACS 

causal categories where significant differences existed.

In some ways, the pattern of human error was similar 

for CFIT and non-CFIT accidents, as skill-based and 

decision errors were the most frequently cited causes 

of both. However, important differences did exist. For 

instance, almost one-third of all CFIT accidents were 

associated with violations of the rules compared with 

just over 12% for non-CFIT accidents, yielding an 

odds ratio of 3.264. Likewise, personal readiness failures (e.g., failing to obtain adequate rest, self medicating, 

etc.), arguably another type of violation only occurring 

external to the cockpit, were over four times more likely 

during CFIT accidents. Adverse mental states (odds ratio = 2.907) and perceptual errors (odds ratio = 1.847) 

were also more prevalent during CFIT than non-CFIT 

accidents. In contrast, physical/mental limitations 

(e.g., the inability to maintain control of the aircraft) 

and failures of crew resource management were more 

likely to occur during non-CFIT than CFIT accidents6.

The Effect of Visual Conditions on CFIT

When discussing CFIT, many safety professionals have 

suggested that these accidents typically occur at night or 

in adverse weather when pilots simply may not be able to 

see their impending collision with the terrain or obstacles. 

However, it now appears that more of these accidents 

occur during VMC (n=867; 61.6%) than IMC (n=501; 

35.6%)7, although the percentage that occurred in VMC 

was considerably less than that observed for non-CFIT 

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6

When interpreting an odds ratio of less than 1, the inverse of the ratio is calculated. For example, the odds ratio associated with physical/

mental limitations was 0.639, indicating that physical/mental limitations were 1/0.639, or roughly 1.5 times more likely to occur during 

non-CFIT than CFIT accidents.

7

The weather conditions at the time of the accident were unknown for 39 (2.8%) CFIT accidents and 62 (0.5%) non-CFIT accidents, while 

the lighting conditions were unknown for two (0.1%) CFIT and four (0.003%) non-CFIT accidents. Weather and lighting combined were 

used to identify visual conditions (impoverished versus clear). When the data were examined in this manner, visual conditions were completely 

unknown for 27 (1.9%) CFIT and 43 (0.3%) non-CFIT accidents. Percentages reported in the text and Figure 5 reflect these data.
14 

accidents (Figure 4, upper left). Furthermore, it appears that 

a greater percentage of CFIT accidents occur during the 

day (n=923; 65.6%) than at dawn or dusk (n=82; 5.8%) 

or even at night (n=400; 28.4%; Figure 4, upper right). 

However, simply looking at lighting conditions without 

considering the weather, or vice-versa, really only presents 

part of the picture. Therefore, we combined the weather 

with the lighting information and examined the percentage of CFIT and non-CFIT accidents occurring during 

visually impoverished (i.e., accidents occurring either at 

night or in IMC) and clear daytime conditions. Yet, even 

when the data were examined in this way (Figure 4, lower 

panel), nearly as many CFIT accidents occurred in clear 

daytime conditions (n=685; 48.7%) as during visually impoverished conditions (n=695; 49.4%). While this finding 

might not have been predicted by those in the GA community, it was not unprecedented given the previous findings 

of Shappell and Wiegmann (1997a) using U.S. Navy/

Marine Corps accident data. In contrast, considerably 

more non-CFIT accidents occurred in clear conditions.

Although there appears to be very little difference in 

the number of accidents that occurred during clear and 

visually impoverished conditions, the question remains 

whether the pattern of human error differed appreciably 

for the different visual conditions. Indeed, the data presented in Figure 5 suggest that in some ways the underlying 

causes are intrinsically different. For instance, those CFIT 

accidents that occurred during visually impoverished 

conditions were more often associated with violations 

of the rules, adverse physiological states, physical/mental 

limitations, and poor crew resource management (Table

5). Perhaps not surprising, aircrew involved in a CFIT accident during visually impoverished conditions were well 

over six times more likely to have committed a violation 

of the rules. They were also five times more likely to have 

been affected by adverse physiological states (e.g., misjudging altitude and spatial disorientation) and more likely 

to mismanage their resources (e.g., failing to obtain an 

adequate preflight weather brief or update prior to departure). Indeed, one could almost envision a crew that fails 

to obtain a weather update prior to takeoff (crew resource 

management) and then encounters weather enroute. 

Then, after choosing to continue into IMC when VFR 

only (violation), they end up misjudging their altitude 

(adverse physiological state) and collide with the terrain.

In contrast to visually impoverished conditions, 

trying to understand why a pilot would collide with 

terrain in clear daytime conditions is somewhat more 

puzzling. However, the odds ratio data may provide 

a clue. It appears that pilots involved with CFIT in 

clear daytime conditions are well over two times more 

likely (1/0.436 = 2.29) to have committed a skill-based 

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15

error than those involved in other types of accidents. 

Given that skill-based behavior is often the result of 

inattention and simple stick-and-rudder skills, perhaps 

they were either not proficient or simply preoccupied 

with other things. In either event, the human errors associated with CFIT in clear and visually impoverished 

conditions are fundamentally different with regard to 

the types of human error more often associated with it. 

Collision With “Terrain/water” Versus Collision 

With “Obstacles”

There was some concern that a definition of CFIT that 

equates collision with terrain/water with collision with 

obstacles might be akin to “comparing apples and oranges,” 

at least from a human factors perspective. To address this 

concern, we examined the pattern of human errors associated with collision with terrain/water (n=826) and that 

with obstacles (n=581). An inspection of Figure 6 revealed 

very few differences between the two types of CFIT, including no differences among the preconditions for unsafe 

acts. In fact, the only differences were among skill-based 

and perceptual errors (Table 6). Specifically, skill-based 

errors were nearly two times more likely (odds ratio = 

1.759) when the collision was with the terrain/water. In 

contrast, collision with obstacles was more often associated with perceptual errors (odds ratio = 1/0.574 or 1.74).

DISCUSSION

Accidents and the tragic loss of life that often accompany them have confronted aviation since the first flights 

of the Wright Brothers. Still, when a healthy pilot flies a 

perfectly good aircraft into the ground, the pundits grow 

eerily quiet. Of all the ways one can crash an aircraft, 

controlled flight into terrain (CFIT) is arguably the 

hardest to explain and therefore begs the question, “Why 

would an experienced aviator fly a perfectly good aircraft 

into the ground” (Shappell & Wiegmann, 1995, 1997a)?

Historically, several explanations for CFIT have been 

offered such as the loss of visual cues at night or during 

IMC, inattention or distraction during periods of high 

workload, or simply poor aviation skills. In response, 

civilian and military organizations have instituted more 

conservative altitude restrictions, provided additional 

safety awareness training, and employed the use of altitude and ground proximity warning systems (GPWS).

Undeniably, these intervention strategies have helped 

save many lives by either requiring aircrews to maintain 

greater separation from hazardous terrain or by alerting 

flight crews to an impending collision with the terrain. 

However, their utility in the realm of general aviation varies dramatically from that of their military or commercial 

aviation counterparts. For instance, most GA enthusiasts 

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Figure 5. Percentage of CFIT accidents occurring in clear versus visually impoverished 

conditions associated with at least one instance of each particular causal category. Statistics associated with violations have been collapsed across type of violation committed. Significant differences (p<.001) are represented by shaded boxes.
16 

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17

do not have the monetary resources of the military or 

commercial sector, making many new technologies such 

as GPWS difficult to afford. Simply enforcing existing 

Federal Air Regulations is likely not the answer either. After 

all, there are more GA aircraft in the U.S. than there are 

military and commercial aircraft combined; not to mention, many of these GA aircraft fly in unrestricted airspace, 

making enforcement a difficult prospect, indeed. So, if the 

availability of terrain warnings or the enforcement of more 

conservative altitude restrictions alone are not likely to 

have a significant affect on GA CFIT, what is the answer?

A major step in addressing this challenge was taken 

by the GA CFIT JSAT and JSIT. In their final report to 

the Joint Steering Committee, the CFIT JSAT identified 55 intervention strategies, finally settling on a “top 

10” that were submitted to the CFIT JSIT. The primary 

goal of the CFIT JSIT was to develop an implementation strategy that identified the resources, responsible 

parties, milestones for implementing the interventions, 

as well as metrics for tracking their success. In the end, 

the CFIT JSIT produced a detailed and prioritized 

implementation plan8 with the following components: 

1. Streamline equipment installation. Terrain avoidance and other equipment have been available for 

some time within military and commercial aviation. 

However, for many GA pilots, such technology is out 

of reach due to cost concerns and the simple fact that 

in many cases new technology in use within military 

and commercial aviation has not been modified for 

GA aircraft. Given “low cost” displays that enhance 

terrain awareness and reduce pilot workload are being 

developed, perhaps the process for certifying the new 

������������������������������������������������������������������������������������

technology can be streamlined within the FAA.

2. Enhance pilot training for CFIT awareness and 

prevention. Specifically, the JSIT recommended that 

Practical Test Standards, Knowledge Tests, and other 

training materials be modified to include knowledge 

of how CFIT occurs and how to prevent it.

3. Establish a General Aviation Safety Council. The idea 

was to establish a council of safety experts from the 

government (FAA, NTSB, and NASA) and industry to 

act as a vehicle for launching safety-related programs 

and distributing information to the GA community 

in a more expeditious and efficient manner.

4. Increase pilot awareness on CFIT accident causes. 

The JSIT proposed establishing a Web page on the 

FAA’s Internet site that would increase pilot awareness 

of the causes of CFIT and relay first-person accounts 

of “near-CFIT” accidents. Also included here was a 

recommendation for the NTSB to begin classifying 

accidents as CFIT or non-CFIT within their accident 

reports to track trends in the data and facilitate future 

analyses.

5. Develop education, awareness, and training modules 

for CFIT prevention. This area focuses on the development of several training modules centered about 

CFIT awareness and risk-taking behavior similar to the 

widely disseminated personal minimums checklist.

6. Standardize and expand requirements for enhancing 

the visibility and detection of wires, support structures, and towers. Surprisingly, there are no standardized criteria for the marking of obstacles or hazards. 

Therefore, it was proposed that a national standard 

for marking wires and towers be developed, as well as 

8

For a more thorough discussion of each of these components, see Federal Aviation Administration (2000). General aviation controlled flight 

into terrain Joint Safety Implementation Team: Final Report.
18 

a passive (e.g., visual markings) and active (e.g., avionics equipment capable of sensing obstacles) means of 

detecting wires, towers and other obstacles.

7. Develop routes for GPS waypoints for mountain 

passes. While somewhat controversial, it was recommended that with the advent of GPS, that waypoints 

for safely flying through mountain passes could be made 

available. Some have argued, though, that this would 

only convey a false sense of ease to pilots attempting 

the complex and often demanding task of traversing 

through high-altitude mountain passes.

8. Enhance digital user access terminals (DUATS) to 

provide density altitude advisories. In particular, pilots 

would receive a density altitude advisory at both the 

departure and destination airports, as well as areas 

along their intended route.

Given the scope and detail of the analyses conducted 

in this study using the HFACS framework with a considerably larger pool of accidents, it seemed reasonable 

to examine which of the interventions identified by the 

CFIT JSAT and JSIT would address the human error 

associated with CFIT. Recall that, in the analysis of all 

GA accidents occurring between 1990-98, skill-based 

errors were associated with nearly 3/4 of all the accidents, 

regardless of whether they were CFIT or not. Skill-based 

errors were followed by decision errors (35.1%), physical/

mental limitations (18.3%), and violations of the rules 

(14.3%). It should come as no surprise then, that skillbased errors (76.3%) and decision errors (33.5%) were 

also the most frequently cited form of human errors associated with CFIT accidents as well. More interesting, 

however, were those human errors that differentiated 

CFIT from non-CFIT accidents. For instance, while 

violations and perceptual errors contributed to only 

12.4% and 7.2% of the non-CFIT accidents, respectively, they contributed to 31.6% (violations) and 12.5% 

(perceptual errors) of CFIT accidents. Likewise, adverse 

mental states and personal readiness failures were more 

likely to occur during CFIT than non-CFIT accidents. 

In fact, CFIT accidents were over four times more likely 

to involve a personal readiness failure and three times 

more likely to involve at least one violation of the rules.

So, how do these findings reconcile with the interventions and implementation plan proposed by the CFIT 

JSAT and JSIT? First, it is hard to overlook the fact that 

three out of four CFIT and non-CFIT accidents were 

associated with skill-based errors, even though there 

were no real differences between the two. Certainly 

then, improving basic flight skills through improved 

primary flight and recurrent training would likely have 

an impact on all types of accidents, including CFIT. 

But, in many instances, skill-based errors are that last 

fatal flaw before impacting the ground – particularly 

among CFIT accidents. The real culprit in CFIT typically lies farther upstream in the causal chain of events 

among areas like violations of the rules, perceptual errors, 

adverse mental states, and personal readiness failures.

An examination of the CFIT JSIT’s implementation 

plan reveals that many of the recommendations map very 

well onto these four human error causal categories. For 

instance, it is quite likely that simply by increasing a pilot’s 

awareness of the hazards of excessive risk-taking and other 

causes of CFIT (numbers 2, 4, and 5 above) we can begin 

to reduce the number of violations and personal readiness 

failures committed by GA pilots. Perhaps this is where 

the establishment of a General Aviation Safety Council 

could help as well by organizing the distribution of training and informational materials directed at improving 

pilot awareness of the issues surrounding CFIT. Indeed, 

if we could somehow convince GA pilots that they are 

four times more likely to die if they continue into IMC 

when they are rated for VFR flight only, a significant 

reduction in the number of fatal accidents might be realized (Shappell & Wiegmann, 2002). Perhaps then, they 

might think twice before taking the chance of having an 

accident. In much the same way, we can provide actual 

data, rather than anecdotes and opinions, regarding the 

hazards of flying without adequate rest or when tired 

and fatigued. Indeed, while an individual GA pilots may 

ignore anecdotes as rare events that only happen to the 

other guy, the statistics may convince them otherwise.

Consistent with previous work in the area (Jensen & 

Benel, 1977; Hunter, 2002; O’Hare, 1990), the data 

presented here suggest that any training aimed at the 

reduction of CFIT should also focus on adverse mental 

states like overconfidence, self-induced pressure, and a 

variety of other hazardous attitudes – particularly when 

adverse mental states were nearly three times more likely 

to be associated with CFIT than non-CFIT accidents. 

Furthermore, because violations and personal readiness 

failures are often associated with adverse mental states, 

one would expect that many of the same interventions 

would be effective in combating adverse mental states as 

well. Consider, for example, the pilot who first learns to 

fly. It’s very unlikely that a pilot with less that 100 hours 

total flight time would press through the weather or try 

flying through a mountain pass. But give that same pilot 

three or four hundred hours in the aircraft and the confidence will build to a point where they might be more 

likely to take risks not previously considered. The difficulty is teasing apart skill from overconfidence, because as 

skill improves overconfidence will likely increase as well. 

The unfortunate thing is that a pilot’s overconfidence 

may lead them into a situation that their current skill 

set cannot get them out of. Now, if we could somehow 
19

control that pilot’s overconfidence either through enhanced training or increased awareness of the hazards 

associated with excessive risk taking (see numbers 2, 4, 

and 5 above) then maybe we can reduce the number of 

violations and personal readiness failures committed by 

GA pilots. Ultimately, this would have the effect of driving 

down the number of CFIT accidents in general aviation.

So, how will the interventions developed by the CFIT 

JSAT affect those CFIT accidents associated with perceptual errors and adverse mental states? Arguably, the small 

percentage of CFIT accidents associated with these two 

error forms was unexpected. After all, in many corners, 

CFIT has often been attributed to spatial disorientation 

and visual illusions that occur during visually impoverished 

environments such as those experienced during IMC or at 

night. Nevertheless, only 12.5% of the CFIT accidents examined occurred as the result of perceptual errors. In fact, 

our analyses revealed that nearly as many CFIT accidents 

occurred during daytime VMC as did those occurring in 

visually impoverished conditions (i.e., during IMC or at 

night). It is unclear then, to what extent using technology 

such as a GPWS or other terrain avoidance technology 

(number 1, above) would help. While it can be reasonably 

argued that terrain displays and other warning systems 

would address some of the problems associated with spatial disorientation at night or in the weather, there were 

only 74 (10.6%) CFIT accidents in which a perceptual 

error was committed in visually impoverished conditions. 

Another 101 or 14.7% of the perceptual errors occurred 

during broad daylight where presumably the errors were 

simply misjudging airspeed and altitude or simply not 

seeing obstacles due to inherent limitations in the visual 

system. What may help in these instances would be the 

use of high visibility paint and other enhancing features 

on obstructions (number 6, above) combined with improved visual scan and safety awareness. Nevertheless, 

the development of a low-cost terrain clearance or “look 

ahead” device (number 1, above) may be worth examining.

It should also be noted that, although crew resource 

management failures were infrequent, when they were 

associated with CFIT accidents they were more than four 

times more likely to occur during visually impoverished 

conditions than during daytime VMC. Upon closer inspection, these failures were often the result of GA pilots 

not taking advantage of all the resources at their disposal 

prior to departing or while en route rather than the traditional crew resource management failures associated 

with communication among multi-place crews. Indeed, 

many of these failures were the result of not getting an 

adequate weather update prior to departing or in the air. 

While none of the CFIT JSIT’s eight areas specifically 

deal with this particular issue (albeit, number 8 above, 

does address the need to provide density altitude information), it would not be difficult to emphasize the need 

for frequent weather updates and encourage the use of 

Flight Service Stations while en route within the training recommended to address CFIT. Indeed, the CFIT 

JSAT did include improving the quality and substance 

of weather briefs in their “top-10” list of interventions, 

but it did not make the final list published by the CFIT 

JSIT. Perhaps if they had the information presented 

here, it might have been included in the final report.

Finally, the CFIT JSAT recommended the development of mountain flying advisory materials. While on 

the surface this makes sense (i.e., the perception that 

pilots are simply flying into mountains), not all CFIT 

occur in mountainous terrain. For that matter, a number 

of accidents are not even controlled flight into “terrain” 

in the classical sense. That is, 581 (41%) were actually 

controlled flight into “obstacles.” Arguably, there were very 

few differences in the pattern of human error associated 

with each, with the noted exception of skill-based errors 

that were more likely during collision with terrain/water 

and perceptual errors that were more likely during collision 

with obstacles (presumably because the pilot could not 

perceive them). In both cases, however, the odds ratios 

were not large (i.e., they were less than 2). Perhaps the 

plan to develop routes for GPS waypoints for mountain 

passes (number 7, above) was driven more by the sample 

of accidents the CFIT JSAT examined, since roughly 

50% of those occurred in the mountains. Certainly, if 

that were to be true for our larger sample of 1407 CFIT 

accidents, it would warrant the emphasis the CFIT 

JSAT and JSIT placed on it by including the development of routes for GPS waypoints in mountain passes. 

Unfortunately, that data have yet to be examined fully.

CONCLUSIONS

Regardless of how one examines the data, using root 

cause analysis or a human error framework like HFACS, 

no single intervention will eliminate GA CFIT accidents. 

What is needed is a strategy that combines several interventions into a concerted effort. More important, a means 

to track intervention strategies is required to assess the 

viability of each recommended intervention on specific 

error forms – a proven quality of the HFACS framework.

It appears from our analysis that many, if not all, of the 

interventions developed by the CFIT JSAT and the accompanying implementation plan proposed by the CFIT 

JSIT will address many of the human error causal factors 

associated with GA CFIT accidents. If nothing else, this 

analysis provides further validation of the efforts of the 

two teams. Beyond simple validation, however, HFACS 

provides a means to track specific types of human error. 

What the CFIT JSAT and JSIT were unable to provide 
20 

is a listing of the specific types of human error committed by aircrew involved in CFIT. This analysis provides 

a benchmark of sorts that will enable the FAA and other 

safety organizations to track the effectiveness of these 

interventions on very specific error forms. If for instance, 

we do not see a significant trend downward in the number 

and percentage of CFIT accidents attributable to violations 

and personal readiness failures, we can re-evaluate the effectiveness of the targeted intervention and modify it as 

needed. Better yet, rather than rely on an overall accident 

rate that is affected by a variety of things other than the 

specific interventions put in place to address CFIT, we can 

now focus specifically on CFIT accidents and those human 

errors most prevalent within the causal chain of events.

In summary, the analysis presented here represents 

a first look at the human error associated with GA 

CFIT accidents and is not the final word. While it does 

validate the findings of the GA CFIT JSAT and JSIT, 

it provides much more. In a sense, it puts a face on human error, particularly human error associated with 

CFIT that we simply did not have prior to the HFACS 

analysis. Now that we know what it looks like, we are 

in a better position to find it and cut it off at its roots.

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