Machine-learning stylometric distance methods based on most-frequent-word frequencies are well-accepted and successful in authoship attribution. This study investigates the results of one of these methods, Burrows's Delta, when applied to translations. Basing the empirical results on a number of corpora of literary translations, it shows that, except for some few highly adaptative translations, Delta usually fails to identify the translator and identifies the author of the original instead.