In this paper the authors present comparison results of two most popular classifiers: the Support vector Machine (SVM) and k-Nearest Neighbor (kNN) using medical image database. The classifier kNN is most commonly used image classifier while SVM considered a state of the art classification algorithm. Experiments were performed on IRMA x-ray library dataset, contains 9,000 training x-ray images and 1000 images for testing. The authors concluded with classification results that overall SVM outperforms kNN. In addition, two well suited classification kernel functions, Gaussian Radial Basis function (RBF) and Polynomial (poly) kernel function of SVM are also compared.