This paper analyzes the effect of data-contrast to a backpropagation (BP) network and introduces a data preprocessing algorithm that can improve the efficiency of the standard BP learning. The basic idea is to transform input data to a range that associates the high-slope region of the sigmoid function where a relatively large modification of weights occurs. A simple uniform transformation to such a desired range, however, can lead to a slow and unbalanced learning if the data distribution is heavily skewed. To facilitate data processing on such distribution, the authors propose a modified histogram equalization technique which enhances the sparing between the data points in the heavily concentrated regions of the distribution.