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Faults are going to occur in most power distribution systems. It is sometimes critical to know the cause of the faults as soon they occur so that appropriate action can be taken, fast and efficiently, in order to reduce the cost of distribution system preparation and to increase the security of the power system. Recently, artificial neural networks have been successfully used to recognize the causes of sustained faults in power distribution systems, by using the fault current information collected for each outage. Here, the authors describe a neural-fuzzy hybrid system to identify the causes of temporary faults as well as sustained faults. The generalization ability of the hybrid fault identification system with respect to different system configurations is analyzed and discussed in the paper.