In this paper we introduce a new model of solving pattern recognition tasks called PRISM. The main concept behind PRISM is the slicing of information through multiple planes across different feature axes to generate a number of cells. The number of cells created and their volume depends upon the number partitions per axes. In this context we define resolution as the number of partitions per axes. In this paper we make the following contributions. First, we provide a brief survey of the class separability measures and feature partitioning schemes used for pattern recognition. Second, we define the PRISM framework and the algorithm for data assignment to cells. Third, we detail four important concepts in PRISM: purity, neighbourhood separability, collective entropy, and data compactness. The first two measures define the data complexity, the next measure relates to uncertainty, and the last measure defines the alternative to statistical data variance in the PRISM framework. Fourth, we investigate the variability in the estimates of these measures depending on the placement of partitions on each feature axis. Finally, we give an overview of experimental successes achieved with PRISM in the areas of classification complexity estimation and feature selection.