One of the most important engineering designs involves a decision-making process for determining an optimum process target (mean). Although researchers and practitioners have proposed a multitude of methodologies to determine the optimum process target under various assumptions, it is believed that they have neither thoroughly considered nor accurately captured the customer perception of a product quality. The analysis in this research differs from the previous studies in two ways. First, most previous studies in the area of process target problem have considered the step-loss function as a quality measurement system. Although the step-loss function has been widely used mainly because of mathematical convenience, there is a general consensus that there is a need to obtain a better approximation for the measurement of customer dissatisfaction of product quality. Second, most studies deal with a single quality characteristic to determine the optimum process target. From the customer's viewpoint, however, products are often judged by more than one quality characteristic. To incorporate these ideas, this research develops analytical methods for selecting the most cost-effective process target while considering multiple quality characteristics for a product. An integral component of this research is the use of quality loss functions as a quality-evaluation instrument to measure and quantify costs associated with customer loss. As part of this research, the step-loss function is extended to the case where multiple quality characteristics are present. Furthermore, a well-known Taguchi loss function is also utilized and extended to the case of multiple quality characteristics, and regression-based loss functions is exhibited by the use of empirical data and also extended to the case of multiple quality characteristics. By integrating three multivariate loss functions, such as the step, Taguchi, and regression-based loss functions, into an economic model, this research proposes an enhanced modeling framework for designing the process target setting where multivariate quality characteristics are present. Case Studies are presented for verification, potential cost savings is demonstrated, and comparison studies are made. Finally, the significance of the research is presented and future research directions are studied.