Autism Spectrum Disorder (ASD) is a spectrum of neurodevelopmental conditions characterized by difficulties in social communication and social interaction as well as repetitive behaviors and restricted interests. Prevalence rates have been rising, and existing diagnostic methods are both extremely time and labor consuming. There is an urgent need for more economic and objective automatized diagnostic tools that are independent of language and experience of the diagnostician and that can help deal with the complexity of the autistic phenotype. Technological advancements in machine learning are offering a potential solution, and several studies have employed computational approaches to classify ASD based on phenomenological, behavioral or neuroimaging data. Despite of being at the core of ASD diagnosis and having the potential to be used as a behavioral marker for machine learning algorithms, only recently have movement parameters been used as features in machine learning classification approaches. In a proof-of-principle analysis of data from a social interaction study we trained a classification algorithm on intrapersonal synchrony as an automatically and objectively measured phenotypic feature from 29 autistic and 29 typically developed individuals to differentiate those individuals with ASD from those without ASD. Parameters included nonverbal motion energy values from 116 videos of social interactions. As opposed to previous studies to date, our classification approach has been applied to non-verbal behavior objectively captured during naturalistic and complex interactions with a real human interaction partner assuring high external validity. A machine learning approach lends itself particularly for capturing heterogeneous and complex behavior in real social interactions and will be essential in developing automatized and objective classification methods in ASD.