Recently, there has been a rapid development in computer technology, which has in turn led to develop the fully robotic welding system using artificial intelligence (AI) technology. However, the robotic welding system has not been achieved due to difficulties of the mathematical model and sensor technologies. The possibilities of the fuzzy regression method to predict the bead geometry, such as bead width, bead height, bead penetration and bead area in the robotic GMA (gas metal arc) welding process is presented. The approach, a well-known method to deal with the problems with a high degree of fuzziness, is used to build the relationship between four process variables and the four quality characteristics, respectively. Using these models, the proper prediction of the process variables for obtaining the optimal bead geometry can be determined. Recently, there has been a rapid development in computer technology, which has in turn led to develop the fully robotic welding system using artificial intelligence (AI) technology. However, the robotic welding system has not been achieved due to difficulties of the mathematical model and The possibilities of the fuzzy regression method to predict the bead geometry, such as bead width, bead height, bead penetration and bead area in the robotic GMA (gas metal arc) welding process is presented. The approach, a well-known method to deal with the problems with a high degree of fuzziness, is used to build the relationship between four process variables and the four quality characteristics, respectively. Use these models, the proper prediction of the process variables for obtaining the optimal bead geometry can be determined.