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Under the circumstance that the deficiency of conventional fault diagnosis method relies transformer oil dissolved gas analysis and the need for the further development of future intelligent diagnostic algorithms, this paper introduces the application of two forward neural networks in transformer fault diagnosis based on the research of artificial neural network and do simulation training with a lot of DGA sample data. Firstly, some flaws in the several common transformer fault diagnosis methods are discussed and it comes to the advantage of the artificial neural network after the analysis of the limitation of the modern intelligent diagnosis algorithm.Then the internal fault in the transformer are classified and coded with the consideration of principle and structure of two forward neural network .The corresponding fault diagnosis model is designed respectively and tested in Matlab with the simulation programs.The simulation results show that two kinds of neural network for transformer fault diagnosis identification is relatively ideal and PNN network behaves better when the Sample size is large, which improves the accuracy of transformer fault diagnosis.