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In this paper, we discuss the principles of using back-propagation neural networks for pattern recognition. The basic information on neural networks learning is presented. A comparison is drawn between classification performance of the back-propagation network and other pattern recognition methods. Special attention is paid to preprocessing of the required data. The following problems are discussed in detail: choosing network topology, selecting proper values for learning coefficients, and estimating the quality of learning and recognition processes.