The theory of grey systems is a new technique for performing prediction, relational analysis and decision making in many areas. In this paper, the use of grey relational analysis for optimizing the square hole flanging process parameters with considerations of the multiple response (the average flanging height, regular flanging and maximum strain) is introduced. Various flanging parameters, such as the blank inner radius rb, blank inner width B0, are considered. An orthogonal array is used for the experimental design. Multiple response values are obtained using finite element analysis (FEA). Optimal process parameters are determined by the grey relational grade obtained from the grey relational analysis for multi-performance characteristics (flanging height, regular flanging and maximum strain). Analysis of variance (ANOVA) for the grey relational grade is implemented. The results showed good agreement with the experiment result. Grey relational analysis can be applied in multiple response optimi-zation designs. The theory of gray systems is a new technique for performing prediction, relational analysis and decision making in many areas. In this paper, the use of gray relational analysis for optimizing the square hole flanging process parameters with considerations of the multiple response (the average flanging Various flanging parameters, such as the blank inner radius rb, blank inner width B0, are considered. An orthogonal array is used for the experimental design. Multiple response values ​​are obtained using finite element analysis (FEA). Optimal process parameters are determined by the gray relational grade obtained from the gray relational analysis for multi-performance characteristics (flanging height, regular flanging and maximum strain). Analysis of variance (ANOVA) for the gray relational grade is implemented. The results showed good agreement with the experiment result. Gray relational analysis can be applied in multiple resp onse optimi-zation designs.