Pronunciation by analogy (PbA) is an emerging, data-driven technique with potential application in text-to-speech (TTS) systems, as well as being an influential psychological model of reading aloud. The underlying idea is that a pronunciation for an unknown word (i.e., one not in the dictionary, or lexicon, of the human or machine “reader”) is assembled by matching substrings of the input to substrings of known, lexical words, hypothesizing a partial pronunciation for each matched substring from the lexical knowledge of the “reader,” and concatenating the partial pronunciations. This paper assesses the capability of PbA to derive pronunciations for unknown words of English. As a psychological model, PbA is “under-specified,” that is, the implementor of a simulation of the process faces detailed choices which can only be resolved by trial and error. One goal for this paper is to explore the impact of certain basic implementational choices on the performance of PbA systems. The variables studied are the specific lexical database used as the basis of the analogy process, the way of ranking/scoring candidate pronunciations, and the effect of manual versus automatic alignment of letters and phonemes. When tested with short (monosyllabic) pseudowords previously used in experimental psychology studies, the lowest error rate achieved is 14.3% (for a test set of size 70). We conclude that current PbA systems are at best poor models of pseudoword pronunciation by humans. To assess their suitability for use in a TTS application, in which multisyllabic words will be encountered, the implementations have also been tested with lexical words temporarily removed from the dictionary. The best performance obtained was 93.5% phonemes correct (corresponding to 67.9% words correct) for a 16,280-word dictionary. This is vastly superior to the 25.7% words correct obtained using a set of popular letter-to-sound rules, indicating considerable scope for analogy methods to be exploited in future TTS systems.