BP neural network classifier has a slowly convergence rate, in order to improve the performance of the classifier, there is an improvement in BP algorithm for the problem. The Conditional Log-Likelihood (CLL) is applied into the supervisory neural network classification for the multi-class selection. By using the decomposability of CLL, calculate the conditional probability of the test samples. In the error back-propagation process, increasing or reducing the corresponding weights by using the conditional probabilities, which can simplify the computation in the process of error feedback. In the paper, we test the convergence speed and accuracy for the improved algorithm in the experiment. It illustrates the effectiveness and the practicality of the algorithm.