In this paper, we present a novel approach for supervised codebook learning and optimization with neural networks for bag of words models in visual recognition tasks. We propose a new supervised framework for joint codebook creation and class learning, which learns the codewords in a goal-directed way using the class labels of the training set. As a result, the codebook becomes more discriminative. Two different learning algorithms, one based on error backpropagation and one based on cluster label reassignment, are presented. We evaluate them on the KTH dataset for human action recognition, reporting very promising results. The proposed technique allows to improve the discriminative power of an unsupervised learned codebook, or to keep the discriminative power while decreasing the size of the learned codebook.