In the field of industrial manufacturing, visual pattern inspection is an important task to prevent the inclusion of incorrect parts. There have been demands for such methods able to handle factors caused by positional and rotational alignment, and illumination changes. In this paper, we propose a discrimination method called Trinarized broad-edge and Gray-scale Hybrid Matching (TGHM). The method is highly reliable due to gray-scale cross correlation which has a high pattern discrimination efficiency, with high-speed position and rotation alignment using the characteristics of trinarized broad-edge representation which has high data compressibility and illumination-resistant variability. In an example in which the method is applied to mis-collation inspection equipment of a bookbinding machine, it is confirmed that the processing speed is 24,000 sheets/hour, the error detection rate is 100.0%, and the mis-alarm rate is less than 0.002%, and it is verified that the method is practical.