Previous studies from different laboratories have suggested that qEEG could be useful for distinguishing dementia from normality. Our aims were: (1) to study the ability of qEEG to distinguish dementia among different pathological conditions in ambulatory settings; (2) to compare the ability of classical statistical analysis and of neural networks in classifying qEEG data. We were able to obtain a multiple discriminant function using a training set of patients, which classified correctly more than 91% of the qEEGs from an independent group of patients, with less than 5% of false positives. Kohonen’s neural network was trained with the same set of patients. This unsupervised learning artificial neural network performed the classification of the independent sample with an accuracy comparable to that of the multiple discriminant function. Our results suggest that the use of unsupervised learning algorithms could be an interesting alternative in the classification of data obtained from psychiatric patients where definition of their clinical profile is not always a simple tas