Cochlear nerve discharge patterns in response to the synthesized consonant-vowel stimulus /da/ were collected from a population of 223 auditory-nerve fibers from a single cat. For each nerve fiber, discharges were measured from multiple, independent stimulus presentations, with the means and variances of the post-stimulus time histograms and Fourier transforms of response generated from the ensemble of stimulus presentations. The statistics were not consistent with those predicted via an inhomogeneous Poisson counting process model. Specifically, the synchronized components as measured by the Fourier transforms of post-stimulus time histogram responses have variances that are as much as a factor of 3 times lower than the predicted by the Poisson model. To account for the non-Poisson nature of the statistics, the Markov process model of Siebert/Gaumond was adopted. Using the maximum-likelihood and minimum description length algorithms, introduced by Miller [J. Acoust. Soc. Am. 77, 1452-1464 (1985)] and Mark and Miller [J. Acoust. Soc. Am. 91, 989-1002 (1992)], estimates of the stimulus and recovery functions were computed for each nerve fiber. Then, Markov point processes were simulated with the stimulus and recovery functions generated from these nerve fibers. The statistics of the simulated Markov processes are shown to have almost identical first- and second-order statistics as those measured for the population of auditory-nerve fibers, and demonstrates the effectiveness of the Markov point process model in accounting for the correlation effects associated with the discharge history-dependent refractory properties of auditory nerve response.