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This paper presents a new and efficient integrated neural fuzzy approach for transformer fault diagnosis using dissolved gas analysis. The proposed approach formulates the modeling problem of higher dimensions into lower dimensions by using the input feature selection based on competitive learning and neural fuzzy model. Then, the fuzzy rule base for the identification of fault is designed by applying the subtractive clustering method which is very good at handling the noisy input data. Verification of the proposed approach has been carried out by testing on standard and practical data. In comparison to the results obtained from the existing conventional and neural fuzzy techniques, the proposed method has been shown to possess superior performance in identifying the transformer fault type. [ABSTRACT FROM AUTHOR]