Vibration analysis is the most widely used tool in industrial application for machine’s health condition assessment. Bearings, however, are very sensitive and require special attention. Vibration analysis employs various signal processing methods such as spectral analysis, time-scale, time frequency analysis, etc. these methods are used to analyze bearings’ vibratory behavior by monitoring the evolution of statistical indicators.However, diagnosing the bearing depending on traditional features only isn’t sufficient to assure effective or reliable assessment of the component’ health condition. This paper proposes a multi-features online dynamic classification as a new method for fault detection and health condition monitoring for bearings; this technique uses multiple features, including traditional features extracted from the raw signal, two special features extracted by wavelet analysis, the spectral kurtosis, coupled with a nonlinear principal component analysis and a dynamic classification to capitalize on the hidden information in the time evolution of the features.Through this article, we introduce different measures and techniques used to characterize the health state of rolling, then we deploy a methodology using dynamic classification to detect early defect. To ensure an almost continuous surveillance, this methodology is based on a real-time analysis, and uses specific statistical indicators adapted to the experimental bench. Then, the monitoring of the degradation is achieved through the resulting class of the state of degradation. New parameters such as the speed of the class, the position of the class, the shape of the class will be discussed to inform the state of damage. The suggested methodology is validated by analyzing several fatigue tests from a fatigue bench bearing thrust ball referenced SNR51207.