In general, neural networks cannot match nonlinear systems exactly, neuro identifier has to include robust modification in order to guarantee Lyapunov stability. In this paper input-to-state stability approach is applied to access robust training algorithms of discrete-time neural networks. We conclude that the gradient descent law and the backpropagation-like algorithm for the weights adjustment are stable and robust to any bounded uncertainties.