We have described in this chapter our current results for map separates, based on the RGB clustering algorithm. This method results in a comparable or somewhat lower quality separation then the backpropagation algorithm, but it is faster by a factor of 100. It is suggested that our RGB clustering algorithm is in fact essentially equivalent to a backpropagation algorithm. In the neural network jargon, we can say that we have found the analytic representation for the bulk of the hidden unit layer which results in dramatic performance improvement. This representation can be thought of numerically as a pixelregion look-up table or geometrically as a set of polyhedral regions covering the RGB cube. Further quality improvement of our results will be achieved soon by refining our software tools and by coupling the RGB clustering with the zero-crossing-based segmentation and edge detection algorithms. Zero crossing techniques provide in turn a natural algorithmic connectivity for our intended collaboration with Coherent Research, Inc. on high-level vision and AI/expert systems techniques for map understanding.