A comparison of different signal processing techniques is presented for fault detection in rolling element bearings. The vibration signals of a rotating machine with normal and defective bearings are processed in time, frequency and time-frequency domains. The features obtained from the original and processed signals are used for detection of bearing condition. The roles of different signal processing techniques and parameters on the effectiveness of bearing fault detection are investigated. The procedure is illustrated using the experimental vibration data of a rotating machine.