A bounded scale mixtures of normal (BSMN) distributions is proposed as a novel device for modeling asymmetric and bounded data. Some characterizations and probabilistic properties of the BSMN distributions and an extension to finite mixtures thereof are discussed. Based on a sort of selection stochastic representation, we design a feasible expectation–conditional maximization either (ECME) algorithm to compute the maximum likelihood estimates of model parameters. To showcase the utility of finite mixture models for clustering data and validating the effectiveness of the proposed methodology, we conducted an experiment using a real natural image. The results obtained clearly illustrate the efficacy and value of the proposed distributions over conventional ones..