This paper is concerned with the problem of clustering a set of independently drawn unlabbled samples into homogeneous clusters. Each sample is a data vector drawn from a class with a probability distribution with known structure, and parametrized by a parameter vector a. No priori knowledge is assumed of the parameter set, or the number of available classes. For large data records the MLE is approximately Gaussian. We exploit this asymptotic property, and regard the the MLE set as the unlabelled samples. The clustering problem becomes then that of the identification of a mixture of Gaussian clusteries. We use the generalized mixture likelihood as the clustering metric. This metric was found to perform very close to the Bayes classifier while avoiding the computational burdens associated with the mixture likelihood maximization. The paper also addresses the problem of sparse data and deviation from normality assumption. The method is illustrated for the textured image segmentation.