This article describes an authorship, and more generally document classification, experiment on a pre-existing Dutch corpus of university writings. By measuring linguistic distances using a cross-entropy technique, a technique sensitive not only to the distributions of language features, but also to their relative intersequencing, classification judgments can be made with great sensitivity, significance, confidence, and accuracy. In particular, despite the designed difficulty of the Dutch corpus used, the technique was still able to reliably detect not only authorship, but also subtle features of register, topic, and even the educational attainments of the author. We present evidence suggesting that this technique outperforms more well-known techniques such as function word principle components analysis or linear discriminant analysis, as well as suggest ways in which performance can be improved.