Ear is a new class of relatively stable biometric that is invariant from childhood to early old age (8 to 70). It is not affected with facial expressions, cosmetics and eye glasses. In this paper, we introduce a two-step ICP (Iterative Closest Point) algorithm for matching 3D ears. In the first step, the helix of the ear in 3D images is detected. The ICP algorithm is run to find the initial rigid transformation to align a model ear helix with the test ear helix. In the second step, the initial transformation is applied to selected locations of model ears and the ICP algorithm iteratively refines the transformation to bring model ears and test ear into best alignment. The root mean square (RMS) registration error is used as the matching error criterion. The model ear with the minimum RMS error is declared as the recognized ear. Experimental results on a dataset of 30 subjects with 3D ear images are presented to demonstrate the effectiveness of the approach.