Method for monitoring operating states of a machine by means of models for operating parameters which model operating states such that operating parameters used as input values are recovered as output values of the model, wherein an error dimension is obtained by comparing the output values with the operating parameters that have been input, wherein training a model results in the model being iteratively changed such that an error determined by the error dimension is minimized, comprising the steps of a) measuring at least one operating parameter of the machine during reference operation of the machine in order to obtain reference operating data pertaining to the machine, wherein the machine adopts at least two different reference states during the reference operation; b) using the reference operating data to form a plurality of data blocks which each span data of the reference operating data within different first time periods; c) training a first model for operating parameters on the basis of a first data block of the plurality of data blocks and determining an error limit of the first model; d) applying the first model to a second data block of the plurality of data blocks and if, when said first model is applied to the second data block, an error of the first model is greater than the error limit of the first model, training a second model for operating parameters on the basis of the second data block and determining an error limit of the second model; e) defining a model set which includes the first and the second model; f) applying all of the models of the model set to a further data block of the plurality of data blocks and if, when said models are applied to the further data block, an error of at least one of the models from the model set is greater than the error limit of the respective model, training a further model for operating parameters on the basis of the further data block, determining an error limit of the further model and adding the further model to the model set; g) repeating step f) for all further data blocks of the plurality of data blocks; h) measuring the at least one operating parameter of the machine in normal operation, which is different from the reference operation, in order to obtain normal operating data pertaining to the machine, and using the normal operating data to form at least one third data block which spans data of the normal operating data from at least one second time period; i) applying all of the models of the model set to the at least one third data block and determining that there is an abnormal state on the machine if, when said models are applied to the at least one third data block, an error for any of the models of the model set is greater than the error limit of the respective model. a) measuring at least one operating parameter of the machine during reference operation of the machine in order to obtain reference operating data pertaining to the machine, wherein the machine adopts at least two different reference states during the reference operation; b) using the reference operating data to form a plurality of data blocks which each span data of the reference operating data within different first time periods; c) training a first model for operating parameters on the basis of a first data block of the plurality of data blocks and determining an error limit of the first model; d) applying the first model to a second data block of the plurality of data blocks and if, when said first model is applied to the second data block, an error of the first model is greater than the error limit of the first model, training a second model for operating parameters on the basis of the second data block and determining an error limit of the second model; e) defining a model set which includes the first and the second model; f) applying all of the models of the model set to a further data block of the plurality of data blocks and if, when said models are applied to the further data block, an error of at least one of the models from the model set is greater than the error limit of the respective model, training a further model for operating parameters on the basis of the further data block, determining an error limit of the further model and adding the further model to the model set; g) repeating step f) for all further data blocks of the plurality of data blocks; h) measuring the at least one operating parameter of the machine in normal operation, which is different from the reference operation, in order to obtain normal operating data pertaining to the machine, and using the normal operating data to form at least one third data block which spans data of the normal operating data from at least one second time period; i) applying all of the models of the model set to the at least one third data block and determining that there is an abnormal state on the machine if, when said models are applied to the at least one third data block, an error for any of the models of the model set is greater than the error limit of the respective model.