Condition monitoring (CM) and fault diagnosis of equipments has gained greater attention in recent years, due to the need to reduce the down time and enhance the life/ condition of the equipments. The rolling element bearings (REB) are the most critical components in rotary machines. Hence, bearing fault detection and diagnosis is an integral part of the preventive maintenance activity. Vibration signal analysis provides wide range of information for analysis. So in this paper, vibration signals for four conditions of a deep groove ball bearing namely Normal(N), bearing with defect on inner race(IR), bearing with defect on ball(B), and bearing with defect on outer race(OR) have been acquired from a customized bearing test rig under maximum speed and variable load conditions. Depending on the machinery operating conditions and the extent of bearing defect severity, the measured vibration signals are non-stationary in nature. Non-stationary signals are effectively analyzed by wavelet transform technique, which is a popular and widely used time-frequency technique. The focus of this paper is to select a best possible mother wavelet for applying WT on bearing vibration signals. The two selection criteria includes minimum Shannon entropy criteria (MSEC) and Maximum Energy to Shannon Entropy Ratio criteria R(s). This helps in effective bearing CM using WT.