This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed and classified by several traditional and bio-inspired heuristics. Preliminary results show that some methods are able to attain success rates close to 70% when compared to an expert neurologist. Although still not sufficient to implement a reliable sleep classifier, these are promising results that, together with an analysis via Self-Organizing Maps and ant-based clustering, may help to improve the feature extraction and contribute to a better representation of the different classes' characteristics.