Method for supervising the operation of a technical installation, in which a process engineering process having at least one process step is running, wherein data sets characterising the operation of the installation with values of process variables (L, T, F,...) are acquired in a time-dependent manner and are stored in a data memory, wherein in a learning phase, using historical data sets, - a self-organising map, SOM, is trained for each process step or batch, - for each SOM symptom, threshold values and their tolerances are ascertained for each neuron and stored, and - all permitted temporal profiles of winner neurons are ascertained and stored for each process step or batch for all time stamps, wherein in an evaluation phase - a point in time (tp) is determined, starting from which a prediction of the operation of the technical installation is to take place, - subsequently, using current data sets of the at least one process step, more than one winner neuron profile for the entire process step or batch is determined by means of the winner neuron profiles learned, characterised in that - the most probable of the previously determined winner neuron profiles is determined by ascertaining the symptoms of all past time stamps of this process step or batch and the values of the process variables stored in the neurons of this winner neuron profile after the previously determined point in time (tp) are displayed as the predicted profile on a display unit and thus supports an operator of the technical installation in the installation supervision. wherein data sets characterising the operation of the installation with values of process variables (L, T, F,...) are acquired in a time-dependent manner and are stored in a data memory, wherein in a learning phase, using historical data sets, - a self-organising map, SOM, is trained for each process step or batch, - for each SOM symptom, threshold values and their tolerances are ascertained for each neuron and stored, and - all permitted temporal profiles of winner neurons are ascertained and stored for each process step or batch for all time stamps, - a self-organising map, SOM, is trained for each process step or batch, - for each SOM symptom, threshold values and their tolerances are ascertained for each neuron and stored, and - all permitted temporal profiles of winner neurons are ascertained and stored for each process step or batch for all time stamps, wherein in an evaluation phase - a point in time (tp) is determined, starting from which a prediction of the operation of the technical installation is to take place, - subsequently, using current data sets of the at least one process step, more than one winner neuron profile for the entire process step or batch is determined by means of the winner neuron profiles learned, - a point in time (tp) is determined, starting from which a prediction of the operation of the technical installation is to take place, - subsequently, using current data sets of the at least one process step, more than one winner neuron profile for the entire process step or batch is determined by means of the winner neuron profiles learned, characterised in that - the most probable of the previously determined winner neuron profiles is determined by ascertaining the symptoms of all past time stamps of this process step or batch and the values of the process variables stored in the neurons of this winner neuron profile after the previously determined point in time (tp) are displayed as the predicted profile on a display unit and thus supports an operator of the technical installation in the installation supervision. - the most probable of the previously determined winner neuron profiles is determined by ascertaining the symptoms of all past time stamps of this process step or batch and the values of the process variables stored in the neurons of this winner neuron profile after the previously determined point in time (tp) are displayed as the predicted profile on a display unit and thus supports an operator of the technical installation in the installation supervision.