In the focus of this paper is a comparison of the most relevant prosodic features/feature classes for the classification of boundaries and accents in German and in English. Principal components were computed based on a large prosodic feature vector; these principal components were used as predictor variables in a Linear Discriminant analysis as well as in a Classification and Regression Tree. The number of the most relevant principal components was between three and five; for both languages and for boundary and accent classification alike, most important were principal components modelling duration, in combination with energy, followed by pauses and F0.