The traditional mixture model assumes that, a dataset is composed of several clusters of Gaussian distributions. In real life, however, data often do not fit the restrictions of normality very well. It is likely that data from a single cluster exhibiting non- Gaussian shape characteristics could be erroneously modeled as multiple clusters, resulting in suboptimal inference and decision making. More flexibility is given to the mixture model by generalizing the Gaussian mixture model to use Elliptically-Contoured Distributions. While still symmetric, multivariate Elliptically-Contoured Distributions encompass a wide range of peak and tail characteristics. Distributions that can be generated as special cases include the Power Exponential, Gaussian, Laplace, Student’s T, Cauchy, and Uniform. This generalization makes mixture modeling more robust against nonnormality. Two computational algorithms, GARM and GEM,were adapted to optimize the Elliptically Contoured Mixture model and use results from robust estimation theory in order to data-adaptively regularize both. Finally, the Fisher information matrix and information criterion ICOMP are extended to score the new mixture model. These tools are used simultaneously to select the best mixture model and classify all observations without making any subjective decisions. The performance of the proposed mixture model is first demonstrated on a simulated dataset with extreme overlap. Secondly, the Elliptically-Contoured Mixture model is used on a medical dataset in which, the clinicallydetermined cluster structure are recovered For both datasets, the proposed mixture model substantially improves classification rates over the Gaussian mixture model.