Most information about neuronal properties in primary auditory cortex (AI) has been gathered using simple artificial sounds such as pure tones and broad-band noise. These sounds are very different from the natural sounds that are processed by the auditory system in real world situations. In an attempt to bridge this gap, simple tonal stimuli and a standard set of six natural sounds were used to create models relating the responses of neuronal clusters in AI of barbiturate-anesthetized cats to the two classes of stimuli. A significant correlation was often found between the response to the separate frequency components of the natural sounds and the response to the natural sound itself. At the population level, this correlation resulted in a rate profile that represented robustly the spectral profiles of the natural sounds. There was however a significant scatter in the responses to the natural sound around the predictions based on the responses to tonal stimuli. Going the other way, in order to understand better the non-linearities in the responses to natural sounds, responses of neuronal clusters were characterized using second order Volterra kernel analysis of their responses to natural sounds. This characterization predicted reasonably well the amplitude of the response to other natural sounds, but could not reproduce the responses to tonal stimuli. Thus, second order non-linear characterizations, at least those using the Volterra kernel model, do not interpolate well between responses to tones and to natural sounds in auditory cortex.