Manual visualization-based sleep stage classification is a time-consuming task prone to errors. Since the correct identification of sleep stages is vital for the correct identification of sleep disorders and for the research in this field in general, there is a growing demand for efficient automatic classification methods. However, there is still no symbolic representation of the biomedical signals that leads to a reliable and accurate automatic sleep classification system. This work presents the application of a novel method for symbolic representation of the EEG and evaluates its potential as information source for a sleep stage classifier, in this case a SVM classifier. The data is first analyzed using Self-Organizing Maps (SOM) and a mutual information (MI)-based variable selection algorithm. Preliminary results of sleep data classification provide success rates around 70%. These results are promising since only EEG is used, and there is still room for improvement in this new symbolic representation of the signal.