Many dancers understand that their medium – dance, and more broadly, movement – is not spontaneously generated. The accumulated amalgamation of movement patterns learned from family, culture, teachers, training and observations form a veritable genealogy that finds expression through the moving body. Dance writers, historians and even audience members often classify these movements into styles based on similarities to other forms they have observed. With enough expertise in viewing, one can even identify an individual dancer’s region or mentor, similar to how one might identify regional accents while speaking. In parallel, biomechanists understand that movement contains intrinsic information about the body, and that information can be made readable through motion capture technology. Like writing, an early technology that makes a record of language, motion capture can create a record of the embodied communication that is dance. However, many approaches in movement computing tend to lose, ignore or simplify the cultural context in which movement occurs. This is significant, as dance, like language, is a cultural product. Using motion analysis, can we quantify and analyze the relationships between dance forms in the way linguistics analyzes language? How might that challenge pre-constituted systems of recognition, classification and representation in dance? What might we discover about identity and embodied cultural knowledge? As a professional dancer and biomechanist in Chicago, I propose a biomechanical mapping of dance that employs machine learning techniques to motion capture data. In doing so, I hope to better understand the complex interactions of movement practices, culture and identity in dance.