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Network reconfiguration for maximizing voltage stability is the determination of switching-options that maximize voltage stability the most for a particular set of loads on the distribution systems, and is performed by altering the topological structure of distribution feeders. Network reconfiguration for time-varying loads is a complex and extremely nonlinear optimization problem which can be effectively solved by Artificial Neural Networks (ANNs), as ANNs are capable of learning a tremendous variety of pattern mapping relationships without having a priori knowledge of a mathematical function. In this paper a generalized ANN model is proposed for on-line enhancement of voltage stability under varying load conditions. The training sets for the ANN are carefully selected to cover the entire range of input space. For the ANN model, the training data are generated from the Daily Load Curves (DLCs). A 16-bus test system is considered to demonstrate the performance of the developed ANN model. The proposed ANN is trained using Conjugate Gradient Descent Back-propagation Algorithm and tested by applying arbitrary input data generated from DLCs. The test results of the ANN model are found to be the same as that obtained by off-line simulation. The enhancement of voltage stability can be achieved by the proposed method without any additional cost involved for installation of capacitors, tap-changing transformers, and the related switching equipment in the distribution systems. The developed ANN model can be implemented in hardware using the neural chips currently available.