In corpus linguistics, but also in computational linguistics and information retrieval, there is an increasing demand for the automatic classification of large amounts of text(s). In his research, Biber uses the Multi-Feature/Multi-Dimension (MF/MD) method to obtain a classification of English texts. A major disadvantage of his approach is the heavy reliance on the frequency count of complex grammatical features which are hard to retrieve automatically. In this paper, we investigate whether Biber’s MF/MD method can be used for automatic text classification. For this purpose, the MF/MD method is applied to the ICE-GB corpus, using three different sets of linguistic features. The results indicate that automatic text classification is indeed feasible using word class tags as input for the MF/MD method.