Abstract A statistical classification scheme for a given set of data requires knowledge of the probability distribution of the observations. Traditional approaches to this problem have revolved around chosen various parametric forms for the probability distribution and evaluating these by goodness of fit methods. Among the difficulties with this method are that it is time consuming, it may not lead to satisfactory results and it may lie beyond the statistical expertise of many practitioners. In this paper, the author's consider the use of a recently developed nonparametric probability density estimator in classification schemes with mean squared error loss criterion. Classical parametric approaches are compared to the nonparametric method on simulated data on the basis of the misclassification probability. Real data from the medical and biological sciences are also used to illustrate the usefulness of the nonparametric method.