Artificial neural networks are one of the most commonly used tools for character recognition problems, and usually they take gray values of 2D character images as inputs. In this paper, we propose a novel neural network classifier whose input is ID string patterns generated from the spatial relationships of primitive structures of Ethiopiccharacters. The spatial relationships of primitives are modeled by a special tree structure from which a unique set of string patterns are generated for each character. Training theneural network with string patterns of different font types and styles enables the classifier to handle variations in font types, sizes, and styles. We use a pair of directional filters forextracting primitives and their spatial relationships. The robustness of the proposed recognition system is tested by real life documents and experimental results are reported.