Acoustic emission method can be used to monitor the friction state of the mechanical seal face.In order to extract the features of acoustic emission signals accurately,a method that used particle swarm optimization (PSO) algorithm to optimize Elman neural network was presented.The friction state of mechanical seal face was identified by this method,and the recognition rate the Elman neural network before and after optimizing by PSO algorithm was compared.The results show that the Elman neural network optimized by PSO algorithm has higher recognition rate of the friction state of mechanical seal face,so the effective monitoring of the friction state of mechanical seal face in real time is realized.