The extreme learning machine(ELM)was used for wear particle recognition of ferrography.With the characteristic parameters of the shape size,color and texture extracted from the abrasive color image as the input of the ELM,with five types of abrasives,the normal sliding abrasive,severe sliding abrasive, spherical abrasive,cutting abrasive,oxide abrasive,as the output of the ELM,an ELMbased abrasives classifier was established.Seventeen feature parameters from three aspects were arranged and combined to establish different models.Through comparative experiments and analysis,the optimal model and abrasive classifier were determined.The performance of the wear particle classifier based on ELM and BP neural network was compared through experiments.The results show that the wear particle classifier based on ELM has an average recognition speed of 150 ms and a maximum recognition accuracy of 96%,the wear particle classifier based on BP neural network has an average recognition speed of 250 ms and a maximum recognition accuracy of 90%,which indicates that the wear particle classifier based on ELM has faster recognition speed and higher accuracy.