Bearings are crucial components in rotating machines, which have direct effects on industrial productivity and safety. To fast and accurately identify the operating condition of bearings, a novel method based on multi?scale permutation entropy ( MPE) and morphology similarity distance ( MSD) is proposed in this paper. Firstly, the MPE values of the original signals were calculated to characterize the complexity in different scales and they constructed feature vectors after normalization. Then, the MSD was employed to measure the distance among test samples from different fault types and the reference samples, and achieved classification with the minimum MSD. Finally, the proposed method was verified with two experiments concing artificially seeded damage bearings and run?to?failure bearings, respectively. Different categories were considered for the two experiments and high classification accuracies were obtained. The experimental results indicate that the proposed method is effective and feasible in bearing fault diagnosis.