Described here are two approaches for estimating the parameters (a-priori probabilities, means, and covariances) of a mixture of normal distributions, given a finite sample X drawn from the mixture. One approach is based on a modification of the EM algorithm for computing maximum-likelihood estimates, while the other makes use of the fuzzy c- means algorithms for locating clusters. The reliability, accuracy, and efficiency of these two algorithms are compared using samples drawn from three artificial univariate normal mixtures of two classes.