In this paper, a method for classifying objects based on the use of autoregressive model parameters which are obtained from a time series representation of shape boundaries in digital images of objects is presented. This technique is insensitive to size and is rotation invariant. The objects chosen are four types of aircraft from a digital photograph. Recognition accuracies of more than 90% were obtained for all the pattern classes. All pattern recognition problems involve two random variables, the pattern vector and the class to which it belongs. The interdependence of the two variables is given by the conditional probability density function. The degree of dependence between the pattern vector and the particular class is measured by the “distance”. A simple Bhattacharya distance classifier was used for the purpose.