Automatic pronunciation of unknown words is a hard problem of great importance in speech technology. Proper names constitute an especially difficult class of words to pronounce because of their low frequency of occurrence and variable origin. In this paper, we compare three different data-driven approaches which use a dictionary of (known) proper names to infer pronunciations for unknown names, namely: pronunciation by analogy (PbA), the table look-up method by Weijters, and the ‘improved' table look-up method by Daelemans and van den Bosch. Evaluation is both objective, in which inferred pronunciations are compared with ‘gold standard' dictionary pronunciations, and subjective, in which listeners rated synthesised pronunciations using a 5-point scale. Objective evaluation used a leave-one-out technique on 52,911 names in the CMUDICT dictionary. Results show that PbA achieves the best performance at 63.93% names correct. In the subjective evaluation of 529 different pronunciations of 200 names, 12 listeners rated the pronunciations. Non-parametric tests of significance show that the dictionary pronunciations are rated superior to the automatically-inferred pronunciations; PbA is superior to both table look-up methods, and the ‘improved' table look-up is superior to Weijters' original method (Walsh test, p < 0.005 in all cases).