Arthritis is one of the most common health problems affecting people around the world. The goal of the work presented work is to classify and categorizing hand arthritis stages for patients, who may be developing or have developed hand arthritis, using machine learning. Stage classification was done using finger border detection, developed curvature analysis, principal components analysis, support vector machine and K-nearest neighbor algorithms. The outcome of this work showed that the proposed method can classify subject finger proximal interphalangeal joints (PIP) and distal interphalangeal joints (DIP) into stage classes with promising accuracy, especially for binary classification.