Light series antifriction bearing cages are prone to deformation during the riveting process due to the small diameter of pockets and the relatively large nail hole distance between the two halves, resulting in the defects of riveting skew. Therefore, this paper proposed a pattern recognition method based on image texture features for the accurate identification of cage skew defects. Firstly, a bearing normalization expansion algorithm was improved, which realized the automatic optimization of the starting point of the expansion to avoid missegmentation of the cages, rivets and rolling elements. Secondly, a bearing image cage localization and segmentation algorithm was designed, and 7 cage regions were accurately separated. Finally, the Hu moment and rotation invariant uniform local binary pattern (LBPriu2P,R) were extracted separately as texture features, and the classification model was constructed by combining PCA and SVM. The results showed that the correct recognition rate of the SVM model based on Hu moment and LBPriu2P,R were 85% and 100% respectively. Therefore, the LBPriu2P,R feature combined with the SVM model has a good recognition effect on the bearing cage skew defect. This method was expected to provide a reference for the automatic identification of the defects in the antifriction bearing cage riveting process.