It is difficult to segment and recognize ferrography image precisely due to the complex background,wide size distribution and overlapping debris. An intelligent multi-target wear debris segmentation and recognition method based on the deep neural network model Mask R-CNN was propose to study three kinds of abnormal abrasive,including fatigue spall,severe sliding debris,laminar debris. For feature extraction layer,residual network ResNet50 and ResNet101 with dif- ferent depths were selected for comparative test. The experimental results show that Mask R-CNN+ResNet101 can effec- tively segment and identify ferrographic wear debris of multiple targets,types and sizes under complex background. The average precision of the test set is as high as 76.2%,and the model has good generalization ability.