The dissolved gas analysis (DGA) of transformer oil is the most used diagnosis method for power transformer faults. While various methods have been developed to interpret DGA results such as IEC gas ratio code method, and key gas method, sometimes they fail to determine the faults. In case of transformer with more than one type of fault it happens normally and in some cases, the DGA results cannot be coordinated by the existing codes making the diagnosis unproductive in multiple faults. This paper presents incorporation of intelligent fuzzy technique and IEC gas ratio code to diagnose several faults in a transformer. This revised approach is recommended for fault transformer diagnosis and the suitable actions to be taken. It has been proved to be a very advantageous tool for transformer diagnosis and upkeep planning. For instance, in fact, the gas ratio limit may not be clear, especially when more than single type of fault exist. Hence, between different types of faults, the code should not change sharply through the boundaries. The proposed method is applied to an independent data of different power transformers and various case studies of historic trends of transformer units. This method has been successfully used to identify the type of fault developing within a transformer even if there is conflict in the results of AI technique applied to DGA data.