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Critical information that is related to vital processes of the cell can be revealed comparing several two-dimensional electrophoresis (2DE) gel images. Through up to 10 000 protein spots may appear in inevitably noisy gel thus 2DE gel image comparison and analysis protocols usually involve the work of experts. In this paper we demonstrate how the problem of automation of 2DE gel image matching can be gradually solved by the use of artificial neural networks. We report on the development of feature set, built from various distance measures, selected and grounded by the application of self-organizing feature map and confirmed by expert decisions. We suggest and experimentally confirm the use of k-means clustering for the pre-classification of 2DE gel image into segments of interest that about twice speed-up the comparison procedure. We develop original Multilayer Perceptron based classifier for 2DE gel image matching that employs the selected feature set. By experimentation with the synthetic, semi-synthetic and natural 2DE images we show its superiority against the single distance metric based classifiers.