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In this paper, the features extraction of passive sonar signals and classification recognition of underwater target are introduced. Due to the complexity and non-stationary of underwater signals, the zero-cross ratio is first used to initially classify the noise signal; then the LOFAR spectrum reflecting non-stationary signal is extracted, and during which the wavelet transform is carried out for some classes of signals. Finally, a fuzzy ART neural network is constructed to carry out the classification. Results of the experiment show that, for six-class target 147 running environments, 5000 realistic data of ship, the mean correct ratio achieves 89%. The result obtained is satisfactory.