We use a locally excitatory globally inhibitory oscillator network (LEGION) as a framework for range image segmentation. Each oscillator in the LEGION network has excitatory lateral connections to the oscillators in its neighborhood as well as a connection with a global inhibitor. The lateral connection between two oscillators is established based on the similarity between their feature vectors which consist of the surface normal and curvature at the corresponding pixel locations. The emergent behavior of the LEGION network gives rise to the segmentation result. Unlike other methods, our scheme needs no assumption about the underlying structures in image data and no prior knowledge regarding the number of regions. Experimental results for real range images are presented.