People recognize and characterize patterns to understand the world. Spatial data exhibit distinctive characteristics that render most aspatial recognition and matching methods unsuitable or inefficient. In past decades, a plethora of methods have been developed for spatial pattern recognition and matching to account for these spatial characteristics. This entry first focuses on the methods of spatial pattern recognition, including an overview of the basic concepts and common types. Methods for spatial pattern matching are then introduced. An example scenario of the distribution of tree species in the Arbuckle Mountains of south-central Oklahoma illustrates covered concepts. The entry concludes with brief remarks on continuing challenges and future directions in spatial pattern recognition and matching in the Big Data and artificial intelligence era.