Abstract: The following study presents the validation of an application developed in MATLAB® for data augmentation which allows to improve the training of convolutional neural networks. The validation is done by comparing the accuracy percentages in the prediction of a trained convolutional neural network with five databases augmented in a different way which allows to determine the characteristics of the training images that produce an increase in network recognition capacities. Each network trained was evaluated by confusion matrices and compared their activations against a test image where it was found that the network with the greatest recognition capacity depends on the changes generated by the data augmentation in the original images (rotations, crops, background changes) as well as the ratio of augmented images and the number of original images used by the data augmentation algorithm developed to produce a new database.