This paper deals with the problem of fault diagnosis for identifying a single fault when the number of assumed faults is larger than that of predictive variables. Principal component analysis (PCA) is employed to isolate and identify a single fault. PCA is a method to extract important information as reducing the number of large dimension in a process. The patterns of all assumed faults can be recognized by PCA and these can be employed whether a new fault is one of predefined faults or not. Through PCA, empirical models for analyzing patterns can be trained. When a single fault occurs, the pattern generated by PCA can be obtained and this is used to identify a fault. The performance of the proposed approach is illustrated in the actuator benchmark problem.