Automatic text classification has gained huge popularity with the advancement of information technology. Bayesian method has been found highly appropriate for text classi- fication but it suffers from a number of problems. When there is large number of categories, lack of uniformity in training data becomes a big problem. Some nodes may get less training documents, while other may get a very large number. Therefore, some nodes are biased over oth- ers. Besides, presence of noise data or outliers also cre- ates problems. Moreover, when documents are very small, just like a line item describing a product, the problem be- comes more difficult. In this paper we describe a method that combines Naive Bayesian text classification technique and neural networks to handle these problems. We start with a Naive Bayesian classifier, which has the linear sep- arating surfaces. We modify the separating surfaces using neural network to find better separating surfaces and hence better classification accuracy over validation data.