This work examines the application of hybrid systems based on Artificial Neural Networks (ANN) and Expert Systems (ES) in detecting and diagnosing faults in Electrical Systems. The research consists of three main parts: the study of cases. In the study of problem, was examined, the importance of detecting and diagnosing faults in Electrical Systems concentrating in Electric Systems equipped with a large quantity of alarms. These alarms may occur simultaneously. Consequently, it is difficult for the electrical systems´operators to identify the faults and decide the corrective action to be adopted, resulting, eventually, in diagnosis erros. The analysis of the problem also involved some interviews with experts in the area, in order to absorb the specific knowledge about the problem, and design the best solution to solve it. The modeling of the Hybrid System involved two parts: the detection of faults, executed by a group of ANNs; and the diagnosis of the detected faults, fulfilled by the ES. In the detection module, a group of four ANNs, each one specialized in an electrical system component (generators, transformers, buses and transmission lines) maps groups of alarms in the specific faults. Therefore, this is a typical pattern classification problem, where each neural network is trained by using the error backpropagation algorithm. The training patterns, produced by experts in the area, consist of the combination of 149 alarms for a total of 184 simple faults and 14 normal operation situations. After training, the ANNs are tested with new samples of alarms, reflecting a certain configuration of the electrical system during the observation period. The ES module, responsible for the diagnosis, receives the ANNs outputs related to the detected faults, and provides to the operator important informations such as: Which alarms were started; the protective equipment involved in the occurrence; the problable reason for the occurrence of the faults; and finally, suggests the corrective action that the operator should perform in order to solve the problem. This information, not available in the ANNs outputs, can be obtained through the application of a set of production rules in a data base, containing the specific knowledge that were extracted from the experts in the area. The simulation environment was developed in a PC plataform. The ANNs, were implemented in MatLab Vers.4.2 and the ES in Delphi. The case studies, applied about 1000 test patterns corresponding to the situation of the 149 alarms. These data, provided by experts of the electrical sector, are adapted from real situations to the dimensions of the Electrical System adopted. In the tests performed, the Hybrid System is submitted to a group of alarms, affected or not by the noise, and reply with suggestions regarding corrective actions that can be adopted by the operator. Various tests were carried out in the generators, transformers, buses, and transmission lines involving simple and multiple faults in the Electrical Power System. With incidence of up to 10% of noise in the test pattern, the performance in detecting fault is near of 100% and for rates superior of 20%, decreases gradually. The evaluation of experts in the electrical sector shows that, the Hybrid System presents a quicker and safer answer, when compared with traditional processes, totally dependent on the human being.