Classification of 3D objects is becoming an increasingly important research area due to cheap and innovative sensor technology. Shadows, noise, viewing direction, and distance from the sensor all directly affect the quality and amount of surface information provided by the sensor. The recognition approach described in this paper converts surface information, a set of (x,y,z) points, into a discrete 3D binary image. This conversion step processes the surface points using a fuzzy technique to mitigate the effects of noise and minor distortions. These images are then processed by sequences of one or two randomly selected morphological operators. Each of the sequences' output is then fed into a simple transducer to obtain a set of scalar feature values. The feature values are classified using a K nearest neighbor (KNN) classifier that is trained using a sparse number of training samples. Experiments were conducted using the Air Force Research Laboratory's E3D data and experimental protocol. Experimental results for the tank classification problem using 10 tanks and 26 confusers are presented. The results show the combination of morphological processing and KNN classifier produced consistently good performance under variations in noise, viewing angle, or distance.