Method (PRC) for monitoring the operation of a machine (MCH), in particular a motor (ENG) or electrically powered motor (EEG), comprising the following steps: a) training phase (TPH): i. providing training data (TDT) comprising state variables (PHC) of a number of operating points (OPP) of the machine (MCH), ii. recognising and combining operating points (OPP) of the training data (TDT) by means of clustering (CLS) to form operating point clusters (OCL), iii. training a classifier (CLF), which assigns operating points (OPP) to the recognised operating point clusters (OCL), iv. training at least one anomaly recognition model (ARM) for operating anomaly recognition, b) application phase (APH): i. recording operating data (OPD) comprising state variables (PHC) of operating points (OPP) of the machine (MCH) in an operating state, ii. assigning the operating data (OPD) to the operating point clusters (OCL) by means of the classifier (CLF), iii. recognising operating anomalies by means of the at least one anomaly recognition model (ARM), characterised in that an anomaly characteristic value (ASC) is calculated by the anomaly recognition model (ARM), the value of which describes the severity of the anomaly, wherein an anomaly and/or an anomaly type is recognised if the anomaly characteristic value (ASC) exceeds a predetermined threshold value (ATH). a) training phase (TPH): i. providing training data (TDT) comprising state variables (PHC) of a number of operating points (OPP) of the machine (MCH), ii. recognising and combining operating points (OPP) of the training data (TDT) by means of clustering (CLS) to form operating point clusters (OCL), iii. training a classifier (CLF), which assigns operating points (OPP) to the recognised operating point clusters (OCL), iv. training at least one anomaly recognition model (ARM) for operating anomaly recognition, i. providing training data (TDT) comprising state variables (PHC) of a number of operating points (OPP) of the machine (MCH), ii. recognising and combining operating points (OPP) of the training data (TDT) by means of clustering (CLS) to form operating point clusters (OCL), iii. training a classifier (CLF), which assigns operating points (OPP) to the recognised operating point clusters (OCL), iv. training at least one anomaly recognition model (ARM) for operating anomaly recognition, b) application phase (APH): i. recording operating data (OPD) comprising state variables (PHC) of operating points (OPP) of the machine (MCH) in an operating state, ii. assigning the operating data (OPD) to the operating point clusters (OCL) by means of the classifier (CLF), iii. recognising operating anomalies by means of the at least one anomaly recognition model (ARM), characterised in that an anomaly characteristic value (ASC) is calculated by the anomaly recognition model (ARM), the value of which describes the severity of the anomaly, wherein an anomaly and/or an anomaly type is recognised if the anomaly characteristic value (ASC) exceeds a predetermined threshold value (ATH). i. recording operating data (OPD) comprising state variables (PHC) of operating points (OPP) of the machine (MCH) in an operating state, ii. assigning the operating data (OPD) to the operating point clusters (OCL) by means of the classifier (CLF), iii. recognising operating anomalies by means of the at least one anomaly recognition model (ARM), <EOS>