Whilst the extraction of teeth (exodontia) remains one of the oldest and most performed surgeries on earth, very little is understood about the procedure itself. Especially in the area of the required movements, torques and forces to remove specific teeth and how these interact with existing tissue. This knowledge gap has been hypothesized to contribute to an increasing referral rate towards Oral and Maxillofacial Surgery (OMFS) practices in the Netherlands for simple extractions due to low confidence in young dentists. The objective of this project is to apply techniques used in imitation-learning in the field of robotics to deconstruct complex movement into smaller fundamental building blocks called movement primitives (MPs) to deepen the understanding of exodontia. To achieve this an existing dataset was used consisting of high resolution force-, torque-, position- and rotation-data of extractions. This dataset was collected using a measurement setup consisting of fresh frozen cadaver material, a force torque sensor and a robotic arm in gravity compensation mode. In this paper a novel iterative two staged method for identifying movement primitives is introduced and employed to extract movement primitive information from these extractions in the dataset.