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We report results on rapidly building language models for dialogue systems. Our base line is a recogniser using a grammar network. We show that we can almost halve the word error rate (WER) by combining language models generated from a simple task grammar with a standard speech corpus and data collected from the web using a sentence selection algorithm based on relative perplexity. This model compares very well to a language model using "in-domain" data from a Wizard Of Oz (WOZ) collection. We strongly advocate the use of statistical language models (SLMs) in speech recognisers for dialogue systems and show that costly WOZ data collections are not necessary to build SLMs.