This paper describes a general algorithm for labeling prosodic patterns in speech, which provides a mechanism for mapping sequences of observations (vectors of acoustic correlates) to prosodic labels using decision trees and a Markov sequence model. Important and novel features of the approach are that it allows many dissimilar correlates to be treated in a unified manner to provide more robust labeling, and that it is designed to be a post-word-recognition processing step. Application of the algorithm is illustrated with experimental results for labeling prosodic phrasing and phrasal prominence in two corpora of professionally read speech. The labels produced by the automatic algorithm exhibit agreement with hand-labeled prominence and phrasing that is close to the agreement between different human labelers. >