Aimed at the problems of single feature and low comprehensive utilization of the features of iron spectrum ab- rasive image recognition,a heterogeneous information fusion recognition method for multi-features of abrasive image was proposed. Firstly,based on the pretreatment of the online ferrography images,three statistical features of abrasive grain tex- ture (ASM, entropy, correlation, contrast), color ( mean, variance, slope), and geometry ( 7 invariant moments) were extracted. Secondly,the feature data was extracted and normalized by [0,1],the kernel parameters were determined by hypersphere spacing method,and multi-class SVM based on single feature was used to realize multi-class wear recognition based on single feature. Finally,based on single feature recognition,the soft decision basic probability assignment (PBA) function required to three features was constructed through posterior probabilities,by using hypersphere multi-class SVM and DS evidence theory combination method, the heterogeneous feature fusion of ferrography image recognition was achieved. The highest recognition rate of the feature fusion method reaches 96. 1%,and compared with the single feature recognition result,it has higher recognition accuracy and can realize the complementary of different features