Premature diagnosis of transformer oil faults enables the operator for diagnosing the transformer condition and hence operating the transformer continuously without outage. DGA is one of the most popular chemical tests used for fault diagnosis and there are many techniques which have been developed in this regard. This paper presents a proposed DGA technique for transformer oil fault identification based on the results of the recently published techniques. The proposed technique has been constructed based on the integration between the outputs of two recently established DGA techniques; Conditional probability and artificial neural network. A total of 532 datasets, obtained from the Egyptian Electricity Transmission Company (EETC) with known faults, have been used for designing and testing the proposed technique. The proposed fault diagnostic technique's accuracy attained 86.6 %, which is higher than the results of the combined techniques; 81.7% for ANN and 82.8% for conditional probability technique. The proposed developed technique came to the conclusion that integrating several DGA techniques, with higher accuracy, enhances fault detection's overall accuracy.