In many manufacturing settings, the determination of the optimum process target (mean) is one of the most important decision-making problems since it directly affects a process defective rate, material cost, scrap or rework cost, and the loss to the customer due to the deviation of a product performance from the customer-identified product target value. A quality loss function approach using conventional quality loss functions, such as step-loss and Taguchi loss functions, has been extensively used to determine the optimum process target mainly due to a mathematical convenience. When historical data concerning the costs associated with product performance are available, a quality loss function using a well-established statistical method, such as regression analysis, may be a more practical alternative procedure. This article shows how the regression-based quality loss function is developed and efficiently employed to determine the optimum process target. A case study is presented.