The paper presents a concurrent algorithm for remote sensing applications that provides signi®cant performance and image quality enhancements over conventional uniprocessor principal component transform (PCT) techniques. The algorithm combines spectral angle classi®cation, principal component transform, and human centered color mapping. It is evaluated from an image quality perspective using images collected with the hyper-spectral digital imagery collection experiment (HYDICE) sensor, an airborne imaging spectrometer. These images correspond to foliated scenes taken from an altitude of 2000±7500 m at wavelengths between 400 nm and 2.5 lm. The scenes contain mechanized vehicles sitting in open ®elds as well as under camou¯age. The algorithm operates with close to linear speedup on shared memory multiprocessors and can be readily extended to operate on multiple, low-cost PC-style servers connected with high-performance networking. A simple analytical model is outlined that allows the impact on performance of practical, application-speci®c properties to be assessed. These properties include image resolution, number of spectral bands, increases in the number of processors, changes in processor technology, networking speeds and system clock rates.