Method for analysis of the operation of the multistage compressor of a gas turbine having a multiplicity of compressor stages and/or for monitoring a gas turbine, wherein one or more neural networks is or are trained on the basis of normal operation of the gas turbine, wherein: - at least one dynamic pressure signal is measured by at least one pressure sensor in or on the compressor of the gas turbine, and one or more operating parameters (V1, V2, V3, V4) of the gas turbine is or are measured by one or more further sensors (4, 5, 6, 7) during normal operation of the gas turbine, and/or a dynamic pressure signal as well as one or more operating parameters (V1, V2, V3, V4) of the gas turbine, which were measured during normal operation of the gas turbine are read in; - the dynamic pressure signal is subjected to a frequency analysis, as a result of which one or more parameters of the frequency spectrum of the pressure signal is or are determined; - one or more neural networks are trained on the basis of the measured operating parameter or parameters (V1, V2, V3, V4) and the parameter or parameters of the frequency spectrum of the pressure signal, which have, as input variables, the measured operating parameter or parameters (V1, V2, V3, V4) and the parameter or parameters of the frequency spectrum of the pressure signal and, as output variables, have at least one diagnosis characteristic value, which represents a probability measure of the presence of normal operation of the gas turbine, as a function of the input variables, characterized in that a characteristic frequency band (F1, F2, F3, F4) is determined on the basis of the rotation speed of the gas turbine and the number of guide vanes and rotor blades in the relevant compressor stage as a parameter of the frequency spectrum for each compressor stage, and, for each characteristic frequency band, the energy component of the pressure signal contained therein, in particular the root mean square value, and/or the amplitude maximum and/or a plurality of adjacent amplitude maxima of frequency lines within the characteristic frequency band (F1, F2, F3, F4) are calculated for use as input variables for the neural network or networks. - at least one dynamic pressure signal is measured by at least one pressure sensor in or on the compressor of the gas turbine, and one or more operating parameters (V1, V2, V3, V4) of the gas turbine is or are measured by one or more further sensors (4, 5, 6, 7) during normal operation of the gas turbine, and/or a dynamic pressure signal as well as one or more operating parameters (V1, V2, V3, V4) of the gas turbine, which were measured during normal operation of the gas turbine are read in; - the dynamic pressure signal is subjected to a frequency analysis, as a result of which one or more parameters of the frequency spectrum of the pressure signal is or are determined; - one or more neural networks are trained on the basis of the measured operating parameter or parameters (V1, V2, V3, V4) and the parameter or parameters of the frequency spectrum of the pressure signal, which have, as input variables, the measured operating parameter or parameters (V1, V2, V3, V4) and the parameter or parameters of the frequency spectrum of the pressure signal and, as output variables, have at least one diagnosis characteristic value, which represents a probability measure of the presence of normal operation of the gas turbine, as a function of the input variables,