The bearing is the key component in a rotating machine. It is important to assess the performance degradation of bearings for realizing proactive maintenance and near-zero downtime. In this paper, a methodology based on the zero crossing characteristic features and a coupled hidden Markov model is introduced for estimating bearing performance degradation. Zero crossing features are time domain representations of the vibration signature in the spectrum domain. They discover the change of bearing performance. When zero crossing features are extracted, a coupled hidden Markov model is employed to assess the performance degradation quantitatively. Results from a bearing accelerated life experiment validate the feasibility and effectiveness of the proposed method.