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This paper presents a different view on the issue of local minima and introduces a new search method for Backpropagation learning algorithm of Multi-Layer Perceptrons (MLP). As in conventional point of view, Back-propagation may be trapped at local minima instead of finding the global minimum. This concept often leads to less confidence that people may have on neural networks. However, one could argue that most of local minima may be caused by the limitation of search methods. Therefore a new search method to address this situation is proposed in this paper. This new method, "retreat and turn", has been applied to several different types of data alone or combined with other techniques. The encouraging results are included in this paper.