This paper investigates phase-lock coding of frequency in the auditory system. One objective with the current model was to construct an optimal central estimation mechanism able to extract frequency directly from spike trains. The model bases estimates of the stimulus frequency on inter-spike intervals of spike trains phase-locked to a pure tone stimulus. Phase-locking is the tendency of spikes to cluster around multiples of the stimulus period. It is assumed that these clusters have Gaussian distributions with variance that depends on the amount of phase-locking. Inter-spike intervals are then noisy measurements of the actual period of the stimulus waveform. The problem of estimating frequency from inter-spike intervals can be solved optimally with a Kalman filter. It is shown that the number of inter-spike intervals observed in the stimulus interval determines frequency discrimination at low frequencies, while the variance of spike clusters dominates at higher frequencies. Timing information in spike intervals is sufficient to account for human frequency discrimination performance up to 5000 Hz. When spikes are available on each stimulus cycle, the model can accurately predict frequency discrimination thresholds as a function of frequency, intensity and duration.