In this paper an artificial neural network based technique will be introduce, which is capable to separate the different types of faulty state of the analysed system and generate signs to alarm the user about the failures in the system. The method can detect, separate, and identify the faults in the system. Large datasets were developed to train the separator networks. To speed up the training process of separator network, active learning method was applied. To find the separator mathematical structure weakness, a complex test process was used where the size of the different faults was varied and the performance of the structure was examined. The examination has two parts: first the appearance and termination of the faults were tested; later the estimation of the fault size was checked. The separator technique needs mathematical models of the analysed system. In our case, the models were also based on feed forward neural networks.