Process optimization involves the minimization (or maximization) of an objective function, that can be established from a technical and/or economic viewpoint. In general, the decision variables are subject to constraints such as Valid ranges (max and min limits) as well as constraints related to safety considerations and those that arise from the process model equations. Usually in chemical engineering problems, both the objective function and the constraints are non-linear. Computational methods of non-linear programming with constraints usually have to cope with problems such as numerical evaluation of derivatives (Jacobian, Hessian) and feasibility issues. The basic idea of the optimization method using neural network (NN) is to replace the model equations or plant data by an equivalent NN, and use this NN to carry on a grid search on the region of interest. As an additional benefit, the full mapping of the objective function allows one to identify multiple optima easily, an important feature not presented by conventional optimization methods. Moreover, the constraints are easily treated afterwards since the points with violated constraints can be recognized and classified (according to weak or hard constraints). This approach was applied in some industrial chemical process: the process of nylon-6,6 polymerization in a twin-screw extruder reactor and an acetic anhydride plant.