This paper represents a taboo-based neural networks learning algorithm (TBBP) to improve the function approximate ability of neural networks for nonlinear functions. By using taboo search during the search process in the global search area, the algorithm can escape from the local optimal solutions and get a superior global solution form the neural networks. To show the effectiveness of this algorithm, it has been used for approximating different nonlinear functions.