This work concentrates on a novel method for empirical estimation of generalization ability of neural networks. Given a set of training (and testing) data, one can choose a network architecture (number of layers, number of neurons in each layer etc.), an initialization method, and a learning algorithm to obtain a network. One measure of performance of the trained network is how closely its actual output approximates the desired output for an input that it has never seen before. Current methods provide a `number' that indicates the estimation of generalization ability of the network. However, this number provides no further information to understand the contributing factors when generalization stability is not very good. The proposed method uses a number of parameters to define generalization ability. A set of values of these parameters provides an estimate of generalization ability. In addition, a value of each parameter indicate the contribution of such factors as network architecture, initialization method, and training data set etc. Furthermore, a method has been developed to verify the validity of estimated values of the parameters.