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The high cost for maintaining complex manufacturing process makes it necessary to enhance an efficient maintenance system. For the efficient maintenance of manufacturing process, a precise fault diagnosis should be performed and an appropriate maintenance action should be executed. This paper suggests an intelligent fault diagnosis system using hybrid data mining. In this system, the rules for the fault diagnosis are inferred by hybrid decision tree/genetic algorithm and the most effective maintenance action is recommended by decision network and AHP(Analytical Hierarchy Process). To verify the efficiency of the proposed intelligent fault diagnosis system, we compared the accuracy of the hybrid decision tree/genetic algorithm with that of the general decision tree learning algorithm(C4.5) by using data collected from a coil-spring manufacturing process.