Brain Computer Interfaces (BCIs) are artificial systems that allow the interaction between a person and their environment using the translated brain electrical signals to control any external device. An EEG neurorehabilitation system can combine portability and affordability with good temporal resolution and no health risks to the user. This system can stimulate the brain plasticity, provided that the system offers reliability on the recognition of the motor imagery (MI) tasks performed by the user. Therefore, the aim of this work is the design of a machine learning system that, based on the EEG signal from only C3 and C4 electrodes, can classify MI tasks with high accuracy, robustness to trial and inter-subject signal variations, and reasonable processing time. The proposed machine learning system has four main stages: preprocessing, feature extraction, feature selection, and classification. The preprocessing and feature extraction are implemented by the extraction of statistical, power and phase features of the frequency sub-bands obtained by the Wavelet Packet Decomposition. The feature selection process is effectuated by a Genetic Algorithm and the classifier model is constituted by a Multiple Classifier System composed by different classifiers and combined by a Multilayer Perceptron Neural Network as meta-classifier. The system is tested on six subjects from datasets offered by the BCIs Competitions and compared with benchmark works founded in the literature, outperforming the other methods. In addition, a real BCI system for neurorehabilitation is designed and tested, producing good results as well.