Many process variables in modern manufacturing process have influence on quality of products with complicated relationships. therefore, it is necessary to control multiple quality variables in order to monitor abnormal signals in the process. This study proposes an integrated procedure of self-organizing map (SOM)neural network and case-based reasoning (CBR) for multivariate process control.SOM generates patterns of quality variables. the patterns are compared with the reference patterns in order to deside whether their states are normal or abnormal using the goodness-of-fitness test. For validation, it generates artificial datasets consisting of six patterns, normal and abnormal patterns. Experimental results show that the abnormal patterns can be detected effectively. this study also shows that the CBR procedure enables to keep type 2 error at very low level and reduce type 1 error gradually, and then the proposed method can be a solution for multivariate process control.