A method of measuring a characteristic of the output of an instrument by using one or more test algorithms to produce a confidence health index for the instrument, characterised in that the method comprising the steps of: generating an expected distribution (102,100) of the instrument when it was determined healthy, and an expected distribution of the instrument (104, 110) when unhealthy by modelling based on past behaviour of the instrument; providing by the instrument a series of instrument readings and a data set comprising measurements of a process variable to an analyser; applying, by the analyser, a first test algorithm to the series of instrument readings in isolation to produce a first score; applying, by the analyser, at least a second test algorithm different from the first test algorithm, to the series of instrument readings in conjunction with the data set, to produce at least a second score; wherein the first and the at least second test algorithms comprise pattern recognition test algorithms among: fluctuation level, fluctuation period, number of spikes, value, deviation and moment correlation; wherein said fluctuation algorithms are not applied to generate a score if no fluctuations are detected in the output of the instrument, no fluctuations being detected when the difference between the highest value and the lowest value in the data set is larger than twice the standard deviation; calculating a probability that the instrument is healthy and a probability that the instrument is unhealthy by comparing the series of instrument readings to both of the expected distributions of the instrument; calculating the ratio between the probability that the instrument is healthy and the sum of the probability that the instrument is healthy and of the probability that the instruments is unhealthy to obtain a confidence level indicative of the likelihood of the instrument being unhealthy; and combining the first score, the at least second score and the confidence level to provide the confidence health index for the instrument. generating an expected distribution (102,100) of the instrument when it was determined healthy, and an expected distribution of the instrument (104, 110) when unhealthy by modelling based on past behaviour of the instrument; providing by the instrument a series of instrument readings and a data set comprising measurements of a process variable to an analyser; applying, by the analyser, a first test algorithm to the series of instrument readings in isolation to produce a first score; applying, by the analyser, at least a second test algorithm different from the first test algorithm, to the series of instrument readings in conjunction with the data set, to produce at least a second score; wherein the first and the at least second test algorithms comprise pattern recognition test algorithms among: fluctuation level, fluctuation period, number of spikes, value, deviation and moment correlation; wherein said fluctuation algorithms are not applied to generate a score if no fluctuations are detected in the output of the instrument, no fluctuations being detected when the difference between the highest value and the lowest value in the data set is larger than twice the standard deviation; calculating a probability that the instrument is healthy and a probability that the instrument is unhealthy by comparing the series of instrument readings to both of the expected distributions of the instrument; calculating the ratio between the probability that the instrument is healthy and the sum of the probability that the instrument is healthy and of the probability that the instruments is unhealthy to obtain a confidence level indicative of the likelihood of the instrument being unhealthy; and combining the first score, the at least second score and the confidence level to provide the confidence health index for the instrument.