This paper presents a language model based on n-grams of word groups (categories). The length of each n-gram is increased selectively according to an estimate of the resulting improvement in predictive quality. This allows the model size to be controlled while including longer-range dependencies when these benefit performance. The categories are chosen to correspond to part-of-speech classifications in a bid to exploit a priori grammatical information. To account for different grammatical functions, the language model allows words to belong to multiple categories, and implicitly involves a statistical tagging operation which may be used to label new text. Intrinsic generalization by the category-based model leads to good performance with sparse data sets. However word-based n-grams deliver superior average performance as the amount of training material increases. Nevertheless, the category model continues to supply better predictions for word n-tuples not present in the training set. Consequently, a method allowing the two approaches to be combined within a backoff framework is presented. Experiments with the LOB, Switchboard and Wall Street Journal corpora demonstrate that this technique greatly improves language model perplexities for sparse training sets, and offers significantly improved size vs. performance tradeoffs when compared with standard trigram models.