Abstract: In conventional blind underwater acoustic signal processing, the independent component analysis algorithm is often used to separate linear mixed signals. However, for the more complex nonlinear mixed signal, the independent component analysis algorithm is helpless. To solve this problem, this article applies slow feature analysis to blind underwater acoustic signal processing. In general, the nonlinear mixed signal varies faster than the source signal does, and SFA algorithm can extract slowly varying features from complex nonlinear signals. Through simulation experiment, the nonlinear mixed signals of simple signals and complex underwater acoustic signals are separated. By comparing the source signals and the separated signals, it is found that the output signals of SFA correlate to the source signal highly. It proves that SFA is effective and practicable in the field of nonlinear blind source separation application.