Failure of a large power transformer not only results in the loss of very expensive equipment, but it can cause significant collateral damage as well. Replacement of that transformer can take up to a year if the failure is not catastrophic and can result in tremendous revenue losses and fines. DGA is a technique used to assess incipient faults of the transformer by analyzing specific dissolved gas concentrations arising from the deterioration of the transformer. DGA is used not only as a diagnostic tool but also to stem apparatus failure. Forecasting of dissolved gases content in power transformer oil is very significant to detect incipient failures of transformer early and ensure normal operation of endure power system. In this study Multi class Support vector Machine is proposed to forecast dissolved gases content in power transformer oil, among which cross validation used to determine free parameters of support vector machine. The experimental data from the electric power company are used to illustrate the performance of proposed SVM model.