We have adopted various defect detection systems in the front stage of manufacturing in order to effectively manage the quality of flash memory products. In this paper, we propose an intelligent pattern recognition methodology which enables us to discriminate abnormal wafer automatically in the course of NAND flash memory manufacturing. Our proposed technique consists of the two steps: pre-processing and hybrid clustering. The pre-processing step based on process primitives efficiently eliminates noisy data. Then, the hybrid clustering step dramatically reduces the total amount of computing, which makes our technique practical for the mass production of NAND flash memory.