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Résumé : Backpropagation (BP) algorithm is widely used to solve many real world problems by using the concept of Multilayer Perceptron (MLP).However, major disadvantages of BP are its convergence rate is relatively slow and always being trapped at the local minima.To overcome this problem, Genetic Algorithm (GA) has been used to determine optimal value for BP parameters such as learning and momentum rate and, also for weight optimization.Although GA has successfully improved Backpropagation Neural Network (BPNN) learning, there are still some issues such as longer training time to produce the output and usage of complex functions in selection, crossover and mutation calculation.In this study, Particle Swarm Optimization (PSO) algorithm has been chosen and applied in feedforward neural network to enhance the learning process in terms of convergence rate and classification accuracy.Two experiments have been conducted; Particle Swarm Optimization Feedforward Neural Network (PSONN) and Genetic Algorithm Backpropagation Neural Network (GANN).The results show that PSONN give promising results in terms of convergence rate and classification accuracy compared to GANN.