The objective of this work was to evaluate a group of classificcation algorhytms that allow us to solve the problem of recognition for hand-written digits. We used WEKA tools and algorhytms implemented with it. We worked with MNIST data base which includes 60000 îmages in numbers (de 28x28 pixels) for training and 10000 for validation with exits labeled from O to 9. In order to reduce the high dimensionality of data bases we applied techniques for analysis of principal components (PCA) and extract the most important characteristics with which we made tests.