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The application of statistical methods to Raman spectroscopy is due to the need to analyse arrays of poorly resolved mineral spectra having low symmetry, large unit cells, and so forth. For the diagnosis of spectral changes under the influence of an external factor, as well as for the determination of critical values, statistical methods for the treatment of the spectrum profile provide more accuracy than a peak‐fitting procedure. The following algorithms for calculating statistical ξ parameters are used to parameterise I(ν,T) arrays of temperature‐dependent Raman spectra: Pearson correlation coefficient characterising the similarity/difference of signals; autocorrelation function, proposed earlier for estimating the weighted average width of spectral lines; and skewness and kurtosis, the parameters of the distribution of signal intensity. The ξ(T) relations were analysed for the synthetic and experimental spectra of natural zircon, titanite, and synthetic quartz in the range of T = 80–870 K. For zircon, the differences in the anharmonic behaviour of modes having various properties and symmetry are shown through the use of ξ(T). For quartz, at the temperatures preceding the displacive α–β phase transition, statistical analysis reveals the spectral region of soft modes, allowing their T‐behaviour to be analysed. For titanite, the critical temperature range corresponding to the displacive α–βγ phase transition is determined by differentiating ξ(T). The results show the potential of using a statistical approach for the rapid detection of spectral and temperature regions responding differently to an external effect, as well as for evaluating the critical values of an external parameter.