A suite of model-driven techniques for identification of 3-D quadric surfaces (cones, cylinders, and spheres) in segmented range imagery is presented. These techniques use range data, surface normal calculated on that data, knowledge of geometric characteristics of the various surfaces, and known model parameters to perform the classification. Second derivative quantities such as curvature, which are unreliable in the presence of noise, are avoided. Model information such as radii and vertex angles are used to guide the classification. Hough-based techniques are employed for extraction of spherical and cylindrical parameters, while conic parameters are presented for numerous scenes of both real and synthetic objects including part jumbles, objects in many poses, and noiseless and noisy synthetic objects. Empirical tests reveal that these methods have advantages (e.g. they appear to be very accurate) over previous methods.