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The application of artificial neural networks (ANN) to power systems has resulted in an overall improvement of solutions in many implementations. This paper presents a new approach for adaptive out-of-step detection of synchronous generators based on neural networks. The paper describes the ANN architecture adopted as well as the selection of the input features for training the ANN. A feedforward model of the neural network based on the stochastic backpropagation training algorithm has been used to predict the out-of-step condition. Due to power network configuration changes, the performance of the protective relays can vary. Consequently, an adaptive out-of-step prediction strategy is suggested in this paper. The capabilities of the proposed strategy have been tested through computer simulation for a typical case study. The results reveal acceptable classification performance.