Computer-implemented method for improved identification of a process anomaly in a technical facility, comprising the following: - training a self-organising map in a first preparation step using the historical process data as good states of the facility in a learning phase, - determination and storage of reference path regions in a second preparation step, wherein the reference path regions are defined with the aid of the temporal sequence of neuron hits in the self-organising map and the tolerances of the hits in the learning phase, - storage of threshold values for the Euclidean distances of the good states to the relevant neurons in the self-organising map in the learning phase, - evaluation of the current process data in the facility in the form of a state vector with the aid of the self-organising map trained in the learning phase, - checking the Euclidean distances of the current state vector to the neuron hit as to whether the threshold value determined in the learning phase is exceeded, - checking the current path by comparison with the reference path regions determined in the learning phase and definition of a neuron to be hit with the aid of the relevant reference path region as long as the threshold value was not already exceeded in the check with the neuron hit, - determination of a symptom vector from the current state vector and either the neuron hit or the neuron that is to be hit, wherein the determination is performed taking into account the threshold value of the relevant neuron and wherein a symptom vector different from the zero vector flags the process anomaly and the symptom vector specifies the process anomaly in more detail (SE, UF) and - outputting the symptom vector, characterised in that, - in addition to the sequence of neuron hits along a path in the learning phase, the respective duration of the good states is additionally acquired and stored and - in which, as long as, during the evaluation of the current process states, the threshold value was not exceeded and the path did not deviate, the duration the good states is additionally used to check the duration of the current states and thereby to identify a process anomaly. - training a self-organising map in a first preparation step using the historical process data as good states of the facility in a learning phase, - determination and storage of reference path regions in a second preparation step, wherein the reference path regions are defined with the aid of the temporal sequence of neuron hits in the self-organising map and the tolerances of the hits in the learning phase, - storage of threshold values for the Euclidean distances of the good states to the relevant neurons in the self-organising map in the learning phase, - evaluation of the current process data in the facility in the form of a state vector with the aid of the self-organising map trained in the learning phase, - checking the Euclidean distances of the current state vector to the neuron hit as to whether the threshold value determined in the learning phase is exceeded, - checking the current path by comparison with the reference path regions determined in the learning phase and definition of a neuron to be hit with the aid of the relevant reference path region as long as the threshold value was not already exceeded in the check with the neuron hit, - determination of a symptom vector from the current state vector and either the neuron hit or the neuron that is to be hit, wherein the determination is performed taking into account the threshold value of the relevant neuron and wherein a symptom vector different from the zero vector flags the process anomaly and the symptom vector specifies the process anomaly in more detail (SE, UF) and - outputting the symptom vector, - in addition to the sequence of neuron hits along a path in the learning phase, the respective duration of the good states is additionally acquired and stored and - in which, as long as, during the evaluation of the current process states, the threshold value was not exceeded and the path did not deviate, the duration the good states is additionally used to check the duration of the current states and thereby to identify a process anomaly.