In the last years, the use of machine learning methods has increased remarkably and therefore the research in this field is becoming more and more important. Despite this fact, a high uncertainity when using machine learning models is still present. We have a wide variety of machine learning approaches such as decision trees or support vector machines and many applications where machine learning has been proved useful like medical diagnosis or computer vision, but all this possibilities make finding the best machine learning approach for a given application a time consuming and not welldefined process since there is not a rule that tells us what method to use for a given type of data.We attempt to build a system that, using machine learning, is capable to learn the best machine learning approach for a given application. For that, we are working on the hypothesis that similar types of data will have also the same machine learning approachas best learner. Classification algorithms will be the main focus of this research and different statistical measures will be used in order to find these similarities among the data.