Snapshot data have become ubiquitous, e.g., maps, im- ages and videos. By extracting interesting features from snapshot data and analyzing their relative orientations and proximities, we can discover important structure configu- ration information among groups of features in a snap- shot database. In this paper, we introduce a class of pat- tern called orientation-sensitive patterns, which occur in many applications ranging from weather study, sport game analysis to medical image processing. We examine three approaches to discover orientation-sensitive patterns. We show that the first apriori-based approach is expensive while the second enumeration-based approach is memory intensive. The third approach decomposes an orientation- sensitive pattern into an H-List and a V-List, which greatly simplifies the mining process. Extensive experiment studies show that the third method is more efficient and scal- able than the apriori and enumeration algorithms. We also present case studies on soccer game snapshots to demon- strate the interesting patterns discovered.