In most of the industries related to mechanical engineering, the usage of pumps is high. Hence, the system which takes care of the continuous running of the pump becomes essential. In this paper, a vibration based condition monitoring system is presented for monoblock centrifugal pumps as it plays relatively critical role in most of the industries. This approach has mainly three steps namely feature extraction, classification and comparison of classification. In spite of availability of different efficient algorithms for fault detection, the wavelet analysis for feature extraction and Naïve Bayes algorithm and Bayes net algorithm for classification is taken and compared. This paper presents the use of Naïve Bayes algorithm and Bayes net algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different discrete wavelet families were calculated and compared to find the best wavelet for the fault diagnosis of the centrifugal pump. © 2012 Elsevier B.V.