The enhancement of the machine condition monitoring process is a key issue for reliability improvement. In fact, in order to produce quickly, economically, with high quality while decreasing the risk of production break due to a machine stop, it is necessary to maintain the equipment in a good operational condition. This requirement can be satisfied by implementing appropriate maintenance strategies such as Condition Based Maintenance (CBM) and using updated condition monitoring technologies for faults detection and classification. In this context, a new method for machinery condition monitoring based on Mel-Frequency Cepstral Coefficients (MFCCs) and Support Vector Machine (SVM) is proposed to automatically detect the mechanical faults by maximized the generalization ability. Hence, the purpose is to design an automatic detection system for mechanical components defects based on supervised classification by trained to maximize the margin. The proposed approach consists in a sequence of binary classifications after extracting a set of relevant features such as temporal indicators and MFCC coefficients. The diagnosis accuracy assessment is carried out by conducting various experiments on acceleration signals collected from a rotating machinery under different operating conditions.