During the last years, we have been working on the automatic classification of boundaries and accents in the German VERBMOBIL (VM) project (human-human communication, appointment scheduling dialogues). A sub-corpus was annotated manually with prosodic boundary and accent labels, and neural networks (NN) trained with a large set of prosodic features were used for automatic classification. The classification of boundaries could be improved markedly with a combination of the NN with a language model (LM) that was trained with manually annotated syntactic-prosodic boundary labels in a much larger sub-corpus. Here we show how a combination of NN with LM along similar lines can be used for an improvement of accent classification as well. For the training of the LM, accents are annotated automatically in the transliteration with the help of a rule-based system that uses part{of{speech (POS) as well as other linguistic/phonological information.