The prevalence of autism spectrum disorder (ASD) has risen significantly in the last ten years, and today, roughly 1 in 68 children has been diagnosed. One hallmark set of symptoms in this disorder are stereotypical motor movements. These repetitive movements may include spinning, body-rocking, or hand-flapping, amongst others. Despite the growing number of individuals affected by autism, an effective, accurate method of automatically quantifying such movements remains unavailable. This has negative implications for assessing the outcome of ASD intervention and drug studies. Here we present a novel approach to detecting autistic symptoms using the Microsoft Kinect v.2 to objectively and automatically quantify autistic body movements. The Kinect camera was used to film 12 actors performing three separate stereotypical motor movements each. Visual Gesture Builder (VGB) was implemented to analyze the skeletal structures in these recordings using a machine learning approach. In addition, movement detection was hard-coded in Matlab. Manual grading was used to confirm the validity and reliability of VGB and Matlab analysis. We found that both methods were able to detect autistic body movements with high probability. The machine learning approach yielded highest detection rates, supporting its use in automatically quantifying complex autistic behaviors with multi-dimensional input.