This paper describes a framework for applying traditional data mining techniques to the non-traditional domain of image datasets for the purpose of knowledge discovery. In particular, perceptual association rules, a novel extension of traditional association rules, are used to distill the frequent perceptual events in large image datasets in order to dis- cover interesting patterns. The focus is on spatial associa- tions although the method is equally applicable to associa- tions within or between other dimensions; i.e., spectral, or in the case of video, temporal. A primary contribution is the derivation of image equivalents for the traditional associa- tion rule components, namely the items, the itemsets, and the rules. The proposed approach is modular, consisting of three steps that can be individually adapted to a particular application. First, the image dataset is labeled in a per- ceptually meaningful way using a visual thesaurus. Second, the firrst- and second-order associations are tabulated in a scalable data structure termed a spatial event cube. Finally, the higher-order associations and rules are determined using an adaptation of the Apriori algorithm. The proposed ap- proach is applied to an aerial video dataset to demonstrate the kinds of knowledge perceptual association rules can help discover.