We study a modified version of a computational model of the human peripheral and central auditory system (Wang, K. and Shamma, S.A., 1995; Yang, X. et al., 1992), and examine the validity of its output from two practical perspectives. One considers the well-known Mel-frequency cepstral coefficients (MFCC) as an approximate representation of the physiology-based early auditory processing result. The other allows the derivation of feature vectors from the dimension expanded cortical response of the central auditory system for use in a conventional phoneme recognition task. In addition to confirming the relevancy of the model under an existing statistical speech recognition framework, we conduct a preliminary study of the cortical response in connection with known physiological studies, to find new possibilities in using the auditory model to perform cognitive functions based on a better understanding of the human auditory system. In particular, the cortical response may be a place-coded data set where sounds are categorized according to the regions containing their most distinguishing features. The results of this study encourage us to develop hierarchical, detection-based methods in which this mechanism may be utilized to simulate a variety of human perceptual and cognitive functions.