Bearing is one of important rotary elements used in almost all machinery. This study concerns with fault diagnosis through machine learning approach using acoustic signals (sound) of bearings in good and simulated faulty conditions. The acoustic data was acquired from near field area of bearings using microphone under different operating conditions. Acoustic signals of a bearing contain the dynamic information about its operating condition. Abundant literature reported suitability of vibration signals for fault diagnosis applications, however, not much using sound signals for diagnosis applications. Also, transducers used for measurement of sound are less costly than transducers used for vibration measurement. Hence, usage of sound signals for fault diagnosis applications of bearings found beneficial. The descriptive statistical features were extracted from sound signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using Bayes classifier. The paper also discusses the effect of various parameters on classification accuracy. © KRISHTEL eMAGING SOLUTIONS PVT. LTD.