This paper proposes a new intelligent fault diagnosis method for plant machinery developed by using principal component analysis (PCA) and ant colony optimization (ACO). The non-dimensional symptom parameters in the frequency domain are defined to reflect the features of vibration signals measured in each state. Sensitive evaluation method for selecting good symptom parameters using principal component analysis (PCA) is also proposed for detecting and distinguishing faults in rotating machinery. Moreover, to shorten the convergence time and improve the efficiency of ACO, the method of local search for the ACO is also presented here. Practical examples of diagnosis for V-belt driving equipment used in a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the faults that often occur in V-belt driving equipment, such as pulley defect state, belt defect state, belt looseness state, etc., are effectively identified by the proposed method.