The control chart aims to clarify whether the technological process is under statistical control and to signal the existence of definable causes, keep the state under statistical control, and demonstrate measures to improve the process capability. The control charts work with data obtained from the technological process at approximately regular intervals, which are determined in time. Usually, each subgroup consists of the same technological parameter, which has measurable units and the same range of the subgroup. From each such so-called logical subgroup, which represents a group of random measurements in which the action of only random causes can be assumed, one or more characteristics (e.g., sample arithmetic mean, sample standard deviation, number of discrepancies, and others) are obtained. A control chart is then a graphical tool showing the subgroup's given selection characteristic to its sequence number. According to the international standard STN ISO 8258 and depending on the nature of the monitored quality feature, the classic control charts are divided into [19; 20; 21; 22; 23]:  
* the control charts by measurement,
* the control charts by comparison (see Figure 1). Control charts by measurement are about data that is continuous and measurable. Control charts, by comparison, are usually considered for data describing qualitative properties. Another division distinguishes between control charts (CC) in which:  
* the basic values are specified,
* the basic values are not specified. Basic values mean the specified requirements or the values stated in the technical documentation of the device. The aim is to reveal whether the monitored values plotted in the control chart fluctuate in a larger range than can be attributed to chance. According to the STN ISO 8258 standard, several types of control charts are distinguished by measurement:  
* the chart for the average \(\bar{x}\) and chart for the pre range \(R\) (chart \(\bar{x}-R\)),
* the chart for the average \(\bar{x}\) and chart for standard deviation \(s\) (chart \(\bar{x}-s\)),
* the chart for the average \(\bar{x}\) and chart for the range \(R\) (chart \(\bar{x}-R\)),
* the chart for the individual values \(X\) and chart for moving range \(R_{MR}\) (chart \(X-R_{MR}\)),
* the chart for the moving arithmetic means \(\bar{X}_{RA}\). Each typical control chart contains three baselines: a centerline CL (Center Line), an upper control limit UCL (Upper Control Limit), and a lower control limit LCL (Lower Control Limit). The centerline (CL) represents the average value of the regulated variable when the process is stable. The distance of the control limits from the central line is most often chosen as \(3\sigma\) of the standard deviation of the given sample characteristic on both sides. This means that there will be approximately 99.73% of values within the regulatory limits (the so-called three-sigma rule). The control limits define the band in which the values of the sample characteristics of the individual subgroups lie with a preselected probability. It is assumed that only random causes of process variability affect the investigated process in a given time period. Obviously, the monitored process is under statistical control, i.e., the process is statistically mastered. If the technological process is under statistical control, the control chart provides a continuous verifying method that the technological process has not changed and is statistically mastered [24; 25; 26; 27; 28]. The technological process's instability is indicated by points outside the regulatory limits or by points showing trends or non-random groupings. If such points exist in the control chart, it is necessary to perform process analysis, find and eliminate the identifiable causes that caused the signaled instability. In industrial practice, it often happens that even at a steady state of the technological process, the output variables still do not get the same results.