Background: Movement screens are frequently used to identify abnormal movement patterns that may increase risk of injury and/or hinder performance. However, abnormal patterns are often detected visually based on the observations of a coach or clinician leading to poor inter- and intrarater reliability. In addition, they have been criticized for having poor validity and sensitivity. Quantitative, or data-driven methods can increase objectivity, remove issues related to inter-rater reliability and offer the potential to detect new and important features that may not be observable by the human eye. The combination of motion capture data, pattern recognition and machine learning could provide a quantitative method to better assess movement competency. Purpose: The purpose of this doctoral thesis was to create the foundation for the development of an objective movement screening tool that combines motion capture data, pattern recognition and machine learning. This doctoral thesis is part of a larger project to bring an objective movement screening tool for use in the field to market. Methods: This thesis is comprised of four studies based on a single data collection and a common series of pre-processing steps. Data from 542 athletes were collected by Motus Global, a for-profit biomechanics company, with athletes ranging in competition level from youth to professional and competing in a wide-range of sports. For the first study of this thesis, an online software program was developed to examine the inter- and intra-reliability of a movement screen, with intrareliability being further examined to compare reliability when body-shape was and was not modified. The second study developed the objective movement screen framework that utilized motion capture, pattern recognition and machine learning. Study 3 and 4 assessed different types of input data, classification goals (e.g., skill level and sport played), feature reduction and selection methods, and increasingly complex machine learning algorithms. Results: For Study 1, when looking at inter- and intra-rater reliability of expert assessors during subjective scoring of movements, intra-rater reliability was better than inter-rater reliability. When assessing the effects of body-shape, on average, reliability worsened when body-shape was manipulated. Study 2 provided proof-of-principle that athletes were able to be classified based on skill level using marker-based optical motion capture data, principal component analysis (PCA) and linear discriminant analysis. For Study 3, PCA in combination with linear classifiers outperformed non-linear classifiers when classifying athletes based on skill level; feature selection increased classification rates, and classification rates when using simulated inertial measurement unit data as the input data were on average better than when using marker-based optical motion capture data. In Study 4, athletes were able to be differentiated based on sport played and recurrent neural nets (RNNs) and PCA in combination with traditional linear classifiers were the optimal machine learning algorithms when classifying athletes based on skill level and sport played. Conclusion: This thesis demonstrates that objective methods can differentiate athletes based on desired demographics using motion capture, pattern recognition and machine learning. This thesis is part of a larger project to bring an objective movement screening tool for field-use to market and provides a solid foundation to use in the continued development of an objective movement screening tool.