This paper describes a new algorithm for feature extraction on hyperspectral images based on blind source separation (BSS) and distributed processing. I use Independent Component Analysis (ICA), a particular case of BSS, where, given a linear mixture of statistical independent sources, the goal is to recover these components by producing the unmixing matrix. In the multispectral/hyperspectral imagery, the separated components can be associated with features present in the image, the source separation algorithm projecting them in different image bands. ICA based methods have been employed for target detection and classification of hyperspectral images. However, these methods involve an iterative optimization process. When applied to hyperspectral data, this iteration results in significant execution times. The time efficiency of the method is improved by running it on a distributed environment while preserving the accuracy of the results. The design of the distributed algorithm as well as issues related to the distributed modeling of the hyperspectral data were taken in consideration and presented. The effectiveness of the proposed algorithm has been tested by comparison to the sequential source separation algorithm using data from AVIRIS and HYDICE. Preliminary results indicate that, while the accuracy of the results is preserved, the new algorithm provides a considerable speed-up in processing.