Tooth removal is one of the most performed surgical procedures worldwide. Despite the high amount of tooth removal procedures carried out each year, scientific understanding of these procedures is not present. Knowledge of force and torque behaviour is limited and knowledge about movements has never been subject to scientific research before. This study is an initial attempt to describe the factors that influence tooth removal in terms of forces, torques and movements. In-vitro measurements were performed that resulted in a dataset containing force, torque and movement time series of 181 human demonstrations of tooth extractions. This report showed how feature engineering and classification modelling were employed to find tooth removal explaining parameters in the dataset. The feature engineering process led to numerical features describing the force and movement (rotation) time series. The rotation features were found to be most descriptive in describing differences in tooth removal procedures. This introduced five distinct rotation strategies that grouped the human demonstrations based on similarity of extraction strategy. These groups have been used as classification labels in the supervised learning process. A Naive Bayes algorithm and a Logistic Regression algorithm were implemented as prediction models. These models showed that while the rotation features contributed the most to the prediction performance, there was need for additional force features to reach maximum prediction performance. The results showed how feature engineering and classification modelling are the first steps in understanding the procedure of tooth removal.