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In spite of the great number of diachronic studies in various languages, the methodology for investigating language change has not evolved much in the last fifty years. Following the progressive trends in other fields, in this paper, we argue for the adoption of a machine learning approach in diachronic studies, which could offer a more efficient analysis of a large number of features and easier comparison of the results across different genres, languages and language varieties. We suggest the use of statistical tests as an initial step for feature selection in an approach which uses the F-measure of the classification algorithms as a measure of the extent of diachronic changes. Furthermore, we compare the performance of the classification task after the feature selection made by statistical tests and the CfsSubsetEval attribute selection algorithm. The experiments were conducted on the British part of the biggest existing diachronic corpora of 20th century written English language --- the &'Brown family' of corpora, using 23 different stylistic features. The results demonstrated that the use of the statistical tests for feature selection can significantly increase the accuracy of the classification algorithms.