Osteoarthritis (OA) of the knee is a disease that deteriorates the bones and surrounding soft tissue of the affected joint. Categorisation of the disease into grades of severity is subject to errors of measurement and poor observer agreement. There is an urgent need for automated methods to measure radiographic features and remove, as far as possible, the element of subjectivity in assessment. This project creates a fully automated system to analyse all aspects of the knee in radiographs. The methods evaluate explicit and implicit features of: overall shape, trabecular structure, osteophytes, tibial spines and intercondylar notch, and joint space shape. The project develops the first fully automated osteophyte detection algorithms, improved trabeculae features using raw pixel intensities, and a better analysis of joint space using shape models.This project is the first to combine explicit and implicit features across the whole of the knee, and applies these features to classify radiographs using four main outcomes: current OA, current pain, later onset OA, and later onset pain. The results find a strong current OA classification rate, with an Area Under the ROC Curve (AUC) of 0.904 and weighted kappa of 0.49 (0.48-0.51). The remaining later onset and pain experiments report weaker results; these results suggest that radiographic features in Posterior-Anterior (PA) view radiographs have a weak association with clinical and later onset OA.