Complex knee injuries are common, often resulting from multiple forces (e.g. rotational, varus-valgus loading, anterior/posterior displacement). Identification of the specific injury pattern of the Anterior Cruciate Ligament (ACL) and other knee structures using non-invasive methods may improve pre-operative planning and guide treatment, reduce costs and facilitate high-quality patient care. The main goal of this project is to present a classification system based on a set of non-invasive measures and state-of-the-art machine learning techniques to preempt the exact ACL rupture pattern.