To address the issues of low efficiency and high error rate associated with traditional manual inspection methods for chip electronic components， this paper proposes an automatic detection method and system based on the normalized cross-correlation algorithm. The proposed system integrates the normalized cross-correlation algorithm with a vision-guided four-axis robotic arm and a secondary bottom-view positioning technique to achieve efficient recognition and precise grasping of components. Additionally， an innovative rotational center calibration algorithm is developed to effectively compensate for coaxiality errors in vacuum nozzles. The system is compatible with various types of electronic components， including chip tantalum capacitors， ceramic capacitors in 0402 packages， and SOD-323 surface-mounted diodes， achieving a positioning accuracy of 0.008 5 mm. Experimental results demonstrate that identifying 100 components takes only 68 ms， with a 100% accuracy rate for orientation and polarity recognition. The rotational center calibration reduces coaxiality errors from 0.4 mm to 0.008 mm， component damage and rejection rates are reduced to 0.01% and 0.02%， respectively， and the successful placement rate reaches 99.98%. Compared with the traditional manual inspection， the system increases detection efficiency by over five times， providing a high-precision and high-reliability technical solution for automatic inspection of electronic components and significantly advancing the application of machine vision technology in industrial inspection.