<p>In this paper, a projection neural network model for solving convex multi-objective optimization problem (CMOP) is considered. The CMOP is first converted into an equivalent convex nonlinear single-objective programming problem by the mean of the weighted sum method, where the Pareto optimal solutions (POS) are calculated by diversifying values of weights. A neural network model is then constructed for solving the obtained convex problem. It is shown that the presented neural network model is stable in the sense of Lyapunov and is globally convergent. Simulation results are given to illustrate the global convergence and performance of our proposed model. Both theoretical and numerical approaches are studied. It is illustrated that the numerical results are in good agreement with the theoretical arguments.</p>