The design of a connectionist expert system for the fault diagnosis of a distribution system is proposed. The heuristic rules acquired from distribution dispatchers' experiences are incorporated into the connectionist network which can be utilized as the expert system knowledge base. The data sets used to train the connectionist network are the current waveforms recorded upon the occurrence of miscellaneous faults. The interconnection weights of this network are adjusted by the back-propagation learning algorithm. The back-propagation connectionist network can then be used as an expert system for diagnosing distributiorf faults. A fault is identified and located by recognizing its associated current waveform. The connectionist expert system is built on a 32-bit personal computer. It is tested for diagnosing faults on a real distribution system. From the experimental results, it is found that the connectionist expert system has the following advantages: it is able to perform on-line diagnoses in a very short operation time of less than 1 second, and it makes inferences about fault types and fault locations from the locally recorded current waveforms only. This system is applicable to dispatching centres. It may assist dispatchers in judging emergency situations, which is the first step in the restoration procedure.