Typical aircraft fasteners are characterised by their extensive variety and substantial production volumes. However, the recognition of these components—which vary in size, exhibit complex geometries, and often appear in disordered orientations—remains a pressing challenge in practical applications. To address this issue, a machine vision-based aircraft connector recognition and classification algorithm is proposed. Firstly, Gaussian filtering is employed to eliminate image noise, followed by a dual-threshold binarisation method to extract edge transition zones. Subsequently, an image coordinate system is established to locate connector edges, partitioning the image into four quadrants. The geometric centres of connectors within each quadrant are then positioned, and key parameters of edge transition zone points are calculated. Finally, the components are identified and classified through a tolerance-based visual recognition algorithm, delineation of enclosed measurement regions for the fasteners, a support vector machine preset value algorithm, and a corner point recognition algorithm based on secondary regions of interest. On a machine vision image processing experimental platform, four distinct types of aircraft fasteners—gaskets, threads, retaining rings, and nuts—were employed as test subjects to validate the detection and recognition accuracy. Building upon this, the algorithm′s solution process is demonstrated. Experimental results indicate that the average detection time per image is 2.14 seconds, with an average classification accuracy of 95.02%. Individual part recognition takes 0.54 seconds, with an error rate of only 4.98%. The SIFT and Hu algorithms achieved average classification accuracy rates of 90.29% and 72.42%, respectively, with individual part recognition times of 1.16 seconds and 1.34 seconds. The recognition time differences between the two detection methods were 0.62 seconds and 0.80 seconds, while the accuracy differences were 4.8% and 22.65%. These results demonstrate that the proposed method meets the requirements for rapid and accurate recognition of aircraft fasteners.