This paper proposes a novel distance measurement using multi-Hilbert scans for matching point patterns on images. A modified Hausdorff distance has been widely used for point pattern matching, recognition tasks, and evaluation of medical image segmentation. However, the computation cost increases sharply with the number of feature points or the increase of data sets. Multi-Hilbert Scanning Distance (MHSD) based on sets of one-dimensional points using Hilbert scans is introduced to overcome this problem. MHSD consists of a combination of four directional Hilbert scans and diagonally shifted Hilbert scans. The proposed method was tested on vehicle images and compared with Hausdorff distance, partial Hausdorff distance, and modified Hausdorff distance. Experimental results show that the proposed method outperforms the compared methods.