This paper presents a new hybrid system for speaker independent continuous speech recognition in a large vocabulary task. The hybrid system is a combination of context dependent discrete Hidden Markov Models and artificial neural networks that are trained by an information theory based algorithm. This algorithm maximizes the Mutual Information (MMI) between the network output and the phone descriptions by applying a self-organizing learning approach instead of forcing constrained network outputs. Recognition results have shown that the new hybrid system outperforms a classical k-means-VQ-based HMM-system. For the speaker independent DARPA Resource Management (RM) task (perplexity 60) we report a decrease in word recognition error rate up to 35% (close to the best continuous pdf systems).