Individuals with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. Automatically detecting these movements in real-time using comfortable, miniature wireless sensors could advance autistic research and enable new intervention tools for the classroom that help children and their caregivers monitor and cope with this potentially problematic class of behavior. We present activity recognition results for stereotypical hand flapping and body rocking using data collected from six children with ASD repeatedly observed in both laboratory and classroom settings. In the classroom, an overall recognition accuracy of 88.6% (TP: 0.85; FP: 0.08) was achieved using three sensors. Challenges encountered when applying machine learning to this domain, as well as implications for the development of real-time classroom interventions and research tools, are discussed.