Based on the traditional theory of singular value decomposition (SVD), singular values (SVs) and ratios of neighboring singular values (NSVRs) are introduced to the feature extraction of vibration signals. The proposed feature extraction method is called SV–NSVR. Combined with selected SV–NSVR features, continuous hidden Markov model (CHMM) is used to realize the automatic classification. Then the SV–NSVR and CHMM based method is applied in fault diagnosis and performance assessment of rolling element bearings. The simulation and experimental results show that this method has a higher accuracy for the bearing fault diagnosis compared with those using other SVD features, and it is effective for the performance assessment of rolling element bearings. •A concept of neighboring singular value ratio (NSVR) is proposed.•The results of bearing fault diagnosis based on NSVRs and singular values (SVs) are different.•The method based on selected SV–NSVR and continuous hidden Markov model (CHMM) is proposed.•The proposed method is proved to be valid for bearing fault diagnosis and performance assessment.