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This paper shows how discriminative training can significantly improve classifiers used in natural language processing, using as an example the task of natural language call routing, where callers are transferred to desired departments based on natural spoken responses to an open-ended "How may I direct your call?" prompt. With vector-based natural language call routing, callers are transferred using a routing matrix trained on statistics of occurrence of words and word sequences in a training corpus. By re-training the routing matrix parameters using a minimum classification error criterion, a relative error rate reduction of 10-30% was achieved on a banking task. Increased robustness was demonstrated in that with 10% rejection, the error rate was reduced by 40%. Discriminative training also improves portability; we were able to train call routers with the highest known performance using as input only text transcription of routed calls, without any human intervention or knowledge about what terms are important or irrelevant for the routing task. This strategy was validated with both the banking task and a more difficult task involving calls to operators in the UK. The proposed formulation is applicable to algorithms addressing a broad range of speech understanding, information retrieval, and topic identification problems.