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This paper presents the development of an algorithm based on discrete wavelet transform (DWT) and probabilistic neural network (PNN) for classifying the power system faults. The proposed technique consists of a preprocessing unit based on discrete wavelet transform in combination with PNN. The DWT acts as extractor of distinctive features in the input current signal, which are collected at source end. The information is then fed into PNN for classifying the faults. It can be used for off-line process using the data stored in the digital recording apparatus. Extensive simulation studies carried out using MATLAB show that the proposed algorithm not only provides an accepted degree of accuracy in fault classification under different fault conditions but it is also reliable, fast and computationally efficient tool.