The problem concerned is to explore the possibility of using artificial intelligence techniques, namely neural networks, and design the appropriate neural network-based algorithm to detect signals of interest from multi-channel data recordings. The problem finds application in diagnostic systems of nuclear power plant with liquid-metal fast breeder. The idea of a whole approach is to make an adaptive diagnostic system of acoustic monitoring of a steam generator unit. The system is based on neural network feature extraction and pattern recognition of multi-channel acoustic signals generated by a steam generator unit. In the background noise environment the diagnostic system must detect water leaks in sodium which may occur in the steam generator unit under monitoring.