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This work reports the use of neural networks for pattern recognition in electroencephalographic signals related to intermittent photic-stimulation. Due to the low signal/noise ratio of this kind of signal, it was necessary the use of a spectrogram as a predictor and a chain of LVQ neural networks. The efficiency of this pattern recognition structure was tested for many different configurations of the neural networks parameters and different volunteers. A direct relationship between the dimension of the neural networks and their performance was observed. Results so far encourage new experiments and demonstrate the feasibility of the proposed system for real-time pattern recognition of complex signals.