In this paper we investigate the use of outer ear images for human identification. From the point of view of image processing, ears offer several advantages over complete faces: reduced spatial resolution, a more uniform distribution of colour, and less variability with expressions and orientation of the face. These advantages together with its identification richness, make ear images appropriate to be used as input data for a connectionist system. A new multiple identification method, which combines the results obtained by several neural classifiers using, respectively, features outer ear points, information obtained from ear shape and wrinkles, and macrofeatures extracted by a compression network, is presented. Experimental results yields higher identification rates as well as a more robust framework using this approach as a component of a more general face identification system especially in security applications.