Matching of observed scene features to stored models of known objects, in 2-D and 3-D, is a fundamental step toward general scene interpretation. For these problems, the match is defined in terms of the correspondence between the model features and the scene features, and the transformation that maps the model onto the scene. The technique presented in this paper uses a hybrid genetic algorithm to search for the correspondence, and each correspondence is evaluated by finding the associated transformation. The genetic algorithm is composed of a position based crossover operator, and two mutation operators: a random mutation operator and an assignment based mutation operator. The measure of the match, as defined by a correspondence and a transformation, is made in accordance with the principles of the minimum representation size criteria. Results for models and scenes in large, occluded, and cluttered environments are described. The results are presented for two distinct cases, in the first case the model and scene are specified in 2-D, and in the second case the model is specified in 3-D and the scene in 2-D. The results show the genetic algorithm based search technique to be very efficient and the overall matching technique to be robust in noisy environments.