In this paper a previously proposed method for the automatic construction of a lexicon with pronunciation variants for ASR is further developed and evaluated. The basic idea is to transform a lexicon of canonical forms by means of rewrite rules that are learned automatically on a training corpus of orthographically transcribed utterances. The method is evaluated on the TIMIT corpus, using a speech recognizer incorporating context-independent HMMs and a bigram language model. It appears that reductions of the word error rate of up to 35 % are possible to achieve. However, it also appears that it is more likely to obtain much lower gains.