This paper proposes a synergism of neural networks (NN) and knowledge-based decision support system (KBDSS) to effectively design an intelligent strategic planning system. Since conventional KBDSS becomes inoperative partially or totally when problem deviates slightly from the expected problem-domain, a new DSS concept is needed for designing an effective strategic planning system, where strategic planning environment is usually turbulent and consistently changing. In line with this idea, this paper developes a NN-based DSS, named ConDSS, incorporating the generalization property of NN into its knowledge base. The proposed ConDSS was extensively operated in an experimentally designed environment with three models: BCG matrix, Growth/Gain matrix, and GE matrix. The results proved very promising when encountered with unforeseen situations in comparisons with conventional KBDSS.