Filter tow production is controlled by individual supervision of the main variables based on the determination of the C-pk, which indicates instability when individual values are below 1. This paper describes a process improvement methodology, which uses statistical tools to correlate the production variables, and thus control the variability of the dependent variables. The analysis was performed over several sets of data, identifying dependent variables - yield (g) and tensile strength (daN) - and establishing relationships of cause and effect with the production variables, indicating the possibility of achieving a new form of control through adjusted multiple regression models, resulting in the C-pk of the dependent variables being greater than 1. These results were improved through an industrial test, using DOE 2(k), which led to the conclusion that the process can be supervised by means of the fitted models, ensuring better control and greater stability.