Among various sounds in the environment, natural sounds, such as water sounds and birdsongs, have proven to be highly preferred by humans, but the reasons for these preferences have not been thoroughly researched. This paper explores differences between various natural and urban environmental sounds from the viewpoint of objective measures, especially psychoacoustical parameters. The sound samples used in this study include the recordings of single sound source categories of water, wind, birdsongs, and urban sounds including street music, mechanical sounds, and traffic noise. The samples are analyzed with a number of existing psychoacoustical parameter algorithmic models. Based on hierarchical cluster and principal components analyses of the calculated results, a series of differences has been shown among different sound types in terms of key psychoacoustical parameters. While different sound categories cannot be identified using any single acoustical and psychoacoustical parameter, identification can be made with a group of parameters, as analyzed with artificial neural networks and discriminant functions in this paper. For artificial neural networks, correlations between network predictions and targets using the average and standard deviation data of psychoacoustical parameters as inputs are above 0.95 for the three natural sound categories and above 0.90 for the urban sound category. For sound identification/classification, key parameters are fluctuation strength, loudness, and sharpness.