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Body-worn sensor systems will help to revolutionize the medical field by providing a source of continuously collected patient data. This data can be used to develop and track plans for improving health (more sleep and exercise), detect disease early, and provide an alert for dangerous events (e.g., falls and heart attacks). The amount of data collected by even a small set of sensors running all day is too much for any person to analyze. Signal processing and classification can be used to automatically extract useful information. This paper presents a general classification framework for wireless medical devices and reviews the available literature for signal processing and classification systems or components used in body-worn sensor systems. Examples focus on electrocardiography classification and signal processing for inertial sensors.