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We propose the use of fine-grained part-of-speech (POS) tags as discriminatory attributes for automatic genre classification and report empirical results from an experiment that indicate substantial accuracy gain by such features over the conventional bag-of-words approach through word unigrams. In particular, this paper reports our research to investigate the performance of a fine-grained tag set when tested with the British component of the International Corpus of English. Ten different genre classification tasks were identified and the performance of the tags was evaluated in terms of F-score. Our results show that the use of linguistically fine-grained POS tags produces superior accuracy when compared with word unigrams, particularly for a rich set of 32 different genres with Naïve Bayes Multinominal Classifier. Through a comparison with an impoverished tag set, our results further demonstrate that the superior performance is due to the rich linguistic information embodied in the 400-strong different POS tags.