This paper describes the development of three alternative techniques for the classification of syllable stress in fluent speech. They are based on: (1) neural networks that use contextual syllabic information, (2) first and second order Markov chains that depend on a new dynamic vector quantization approach, and (3) a rule-based approach. Both the neural network and the statistical approach achieved performance above 80%, with the neural networks slightly outperforming the Markov models. Experimental results also show that stress classification could enhance speech recognition.