It is generally desirable to reduce yarn hairiness as much as possible since it causes serious problems in both yarn production and use of yarn in subsequent textile operations. On the other hand, the cost of yarn production should be minimised while satisfying yarn hairiness and yarn strength specifications. In this study, a multiple response optimisation model based on empirical regression models is developed to determine the best processing conditions for spindle speed, yarn twist, and the number of travelers with yarn hairiness, yarn strength and production cost being multiple response variables. Experimental levels for process variables are selected according to a Central Composite Design (CCD) due to its good statistical properties, such as orthogonality and rotatability. Regression analysis of experimental results indicates that the second-order regression model adequately represents yarn hairiness in terms of process variables. Finally, the yarn production cost model and regression models for yarn hairiness and yarn strength are combined into a multiple response optimisation model to determine optimum processing conditions for different yarn quality levels.