Transformers are always under the impact of electrical, mechanical, thermal and environmental stresses that degrade their insulation quality. To avoid the power failure, periodical monitoring of the conditions of transformers is necessary. Results of early detection of fault can provide large savings in operation and maintenance costs and preventing any premature breakdown/failure. In this paper the DGA (dissolved gas analysis) is studied and the different diagnosis methods are discussed. The conventional ratio methods and the artificial intelligence (AI) Diagnostic methods for DGA in electrical transformers are presented. A comparison between the two different diagnoses methods for a certain transformers already existed in the Egyptian electrical network is presented. These results are also compared to the results received from the central laboratories of the ministry of electricity in Egypt. The simulated results show that the ratio of matched results from the artificial intelligence diagnostic methods (using neural network) was higher than the ratio of matched results from the conventional ratio methods.