Centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus providing a significant reduction in life-time costs. Vibration-based condition monitoring and analysis using machine learning approach is gaining momentum. Vibration monitoring can identify a number of potential pump problems such as bearing fault, impeller fault, seal fault, loose joints or fasteners, and cavitation issues. This paper compares the fault classification efficiency of standalone decision tree classifier, standalone rough set classifier with hybrid systems such as decision tree-fuzzy classifier and rough set-fuzzy classifier. The results obtained using standalone systems are compared with the performance of hybrid systems. It is observed that standalone systems outperform the hybrid systems. Copyright © 2012 Inderscience Enterprises Ltd.