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 resulting in a significant reduction in life-time costs. Vibration analysis is a very popular tool for condition monitoring of machinery like pumps, turbines and compressors. The proposed method is based on a novel immune inspired supervised learning algorithm which is known as artificial immune recognition system (AIRS). This paper compares the fault classification efficiency of AIRS with hybrid systems such as principle component analysis (PCA)-Naïve Bayes and PCA-Bayes Net. The robustness of the proposed method is examined using its classification accuracy and kappa statistics. It is observed that the AIRS-based system outperforms the other two methods considered in the present study. Copyright © 2011 Inderscience Enterprises Ltd.