This paper proposes a novel constructive learning algorithm for a competitive neural network. The proposed algorithm is developed by taking ideas from the immune system and demonstrates robustness in the initial experiments reported here for a benchmark problem. Comparisons with results from the literature are also provided. To automatically segment the resultant neurons at the output, a tool from graph theory was used with promising results. General discussions and avenues for future works are also provided. 371372Fritzke, B., Growing cell structures - A self-organizing network for unsupervised and supervised learning (1994) Neural Networks, 7 (9), pp. 1441-1460Zahn, C.T., Graph-theoretical methods for detecting and describing gestalt clusters (1971) IEEE on Comp., C-20, pp. 68-86De Castro, L.N., Von Zuben, F.J., De D Jr., G.A., The construction of a boolean competitive neural network using ideas from immunology (2003) Neurocomputing, 50 C, pp. 51-85De Castro, L.N., Von Zuben, F.J., Immune and neural network models: Theoretical and empirical comparisons (2001) International Journal of Computational Intelligence and Applications (IJCIA), 1 (3), pp. 239-257De Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagKasabov, N., (2002) Evolving Connectionist Systems: Methods and Applications in Bioinformatics, , Brain Study and Intelligent Machines, Springer VerlagHaykin, S., (1999) Neural Networks - A Comprehensive Foundation, , Prentice Hall, 2nd EdKohonen, T., (2000) Self-organizing Maps, , Springer-Verlag, 3rd Ed