Many physiological functions are based on motor rhythmic activities, among them breathing is a vital issue. The method presented here, or 'temporal grid extraction', aims at characterizing the temporal organization of such an activity. Beyond the measurement of the fundamental frequency, defining the successive cycles, some signal processing tools are helpful in order to look for the presence of higher frequency components that potentially structure these cycles. The method is applied to neurograms recorded from frog brainstem preparations, where two cycle types, buccal and lung cycles, may alternate. It relies on: • Continues Wavelet Transform (CWT) for time-frequency maps and frequency profiles • Crosscorrelation analysis for amplitude maps and amplitude profiles • Cycle-by-cycle autocorrelation analysis for autocorrelation maps and autocorrelation profiles Using this method, the maps and profiles have revealed that a common high frequency clock drives both buccal and lung cycles.