Computational approaches to learning aspects of language typically reduce the problem to learning syntax alone, or learning a lexicon alone. These simplifications have led to disconnected solutions and some unreasonable assumptions about inputs to their algorithms. In this paper, we present an approach that exploits a grammar learning algorithm to learn its own alphabet, or lexicon. We present empirical results and categorize the successes and types of errors lexical acquisition approaches encounter.