In automatic speech understanding, the division of continuously running speech into syntactic chunks is a great problem. Syntactic boundaries are often marked by prosodic means. We use syntactic boundaries for disambiguation during parsing. For the training of statistic models for prosodic boundaries large databases are necessary. For the German Verbmobil project (automatic speech--to--speech translation), we developed a labeling scheme for syntactic--prosodic boundaries. Two main types of boundaries (major syntactic boundaries and syntactically ambiguous boundaries) and some other special boundaries are labeled for a large Verbmobil spontaneous speech corpus. We compare the results of classifiers (multi--layer perceptrons and language models) trained on these syntactic--prosodic boundary labels with classifiers trained on perceptual--prosodic and pure syntactic labels. Recognition rates of up to 96% were achieved. We show that the boundary scores computed by these classifiers can suc..