Multi-microelectrode array devices can be used to simultaneously record activity from multiple neurons distributed in a issue slice. One of the brain functions being investigated with microelectrode arrays is the periodic behavior of spontaneously active neurons in the cortex and basal ganglia. However, these recording methods generate several hundred megabytes of data per hour and, currently, there is no efficient and accurate approach for the identification of the repeated pattern. We present an approach tha uses the discrete wavelet transform to accelerate identification of repeating patterns of neural activity. We perform match filtering on the coefficient data, not the time-domain data. Our wavelet approach operates on ? the data but provides similar classification abilities as the time domain correlation.