Phonological disorders affect 10% of preschool and school-age children, adversely affecting their communication, academic performance, and interaction level. Effective pronunciation training requires prolonged supervised practice and interaction. Unfortunately, many children do not have access or only limited access to a speech-language pathologist. Computer-assisted pronunciation training has the potential for being a highly effective teaching aid; however, to-date such systems remain incapable of identifying pronunciation errors with sufficient accuracy. In this paper, we propose to improve accuracy by (1) learning acoustic models from a large children's speech database, (2) using an explicit model of typical pronunciation errors of children in the target age range, and (3) explicit modeling of the acoustics of distorted phonemes.