Standard Backpropagation Algorithm (BP) usually utilizes two term parameters; Learning Rate α and Momentum Factor β. Despite the general success of this algorithm, there are several drawbacks such as existence of local minima, slow rates of convergence and modification of algorithm requires complex computations. In this study, further analysis of proportional factor γ for 3-Term BP is investigated on various scales of datasets; small, medium and large. Experiments are conducted using three UCI dataset; Balloon, Iris and Cancer. The results show that the 3-Term BP outperforms standard BP for small scale data, but does not work well for medium and large scale dataset.