Urolithiasis is a common disease around the world and its incidence has been growing every year. There are various diagnosis techniques based on kidney stone identification aiming to find the formation cause. However, most of them are time consuming, tedious and expensive. The accuracy of the diagnosis is crucial for the prescription of an appropriate treatment that can eliminate the stones and diminish future relapses. This paper presents two effective supervised learning methods to automate and improve the accuracy of the classification of kidney stones; as well as a dataset consisting of kidney stone images captured with ureteroscopes. In the proposed methods, the image features that are visually exploited by urologists to distinguish the type of kidney stones are analyzed and encoded as vectors. Then, the classification is performed on these feature vectors through Random Forest and ensemble K Nearest Neighbor classifiers. The overall classification accuracy obtained was 89%, outperforming previous methods by more than 10%. The details of the classifier implementation, as well as their performance and accuracy, are presented and discussed. Finally, future work and improvements are proposed.