The classification of a large collection of texts into predefined set of classes is an enduring research problem. The comparative study of classification algorithms shows that there is a tradeoff between accuracy and complexity of the classification systems. This study evaluates the Learning Vector Quantization (LVQ) network for classifying text documents. In the LVQ method, each class is described by a relatively small number of codebook vectors. These codebook vectors are placed in the feature space such that the decision boundaries are approximated by the nearest neighbor rule. The LVQ require less training examples and are much faster than other classification methods. The experimental results show that the Learning Vector Quantization approach outperforms the K-NN, Rocchio, NB and Decision Tree classifiers and is comparable to SVMs