Hyperspectral data are characterized by a richness of information unique among various visual representations of a scene by representing the information in a collection of grayscale images with each image corresponding to a narrow interval in the electromagnetic spectrum. Such detail allows for precise identification of materials in the scene and promises to support advances in imaging beyond the visible range. However, hyperspectral data are considerably large and cumbersome to process and efficient computing solutions based on high performance computing are needed. In this paper we first provide an overview of hyperspectral data and the current state of the art in the use of HPC for its processing. Next we discuss the concept of best band selection, a fundamental feature extraction problem in hyperspectral imagery that, besides exhaustive search has only non optimal solutions. We provide an elegant algorithm that performs an exhaustive search for the solution using a distributed, multicore environment and MPI in order to show how using such a solution provides significant improvement over traditional sequential platforms. Additional experiments on the robustness of the algorithm in terms of data and job sizes are also provided.