In this paper, we present an integrated approach for recognizing both the word sequence and the syntactic-prosodic structure of a spontaneous utterance. The approach aims at improving the performance of the understanding component of speech understanding systems by exploiting not only acoustic-phonetic and syntactic information, but also prosodic information directly within the speech recognition process. Whereas spoken utterances are typically modelled as unstructured word sequences in the speech recognizer, our approach includes phrase boundary information in the language model and provides HMMs to model the acoustic and prosodic characteristics of phrase boundaries. This methodology has two major advantages compared to purely word-based speech recognizers. First, additional syntactic-prosodic boundaries are determined by the speech recognizer which facilitates parsing and resolve syntactic and semantic ambiguities. Second - after having removed the boundary information from the result of the recognizer - the integrated model yields a 4% relative word error rate (WER) reduction compared to a traditional word recognizer. The boundary classification performance is equal to that of a separate prosodic classifier operating on the word recognizer output, thus making a separate classifier unnecessary for this task and saving the computation time involved. Compared to the baseline word recognizer, the integrated word-and-boundary recognizer does not involve any computational overhead.