We describe the development of a real-time processing tool for hyperspectral imagery based on off-the-shelf equipment and higher level programming language implementation (C++ and Java). The algorithms we developed are derived from previously introduced spectra matching and feature extraction tools. The first group is based on spectra identification and spectral screening, a method that allows the identification of representative spectra from a data set. The second group is based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). When applied to multidimensional data, PCA linearly transforms them such that the resulting components are uncorrelated and their variance maximized. In ICA, given a linear mixture of statistical independent sources, the goal is to recover these components by producing an unmixing matrix. The effectiveness of the proposed real time algorithms were tested on an in-house system composed of a commercially available hyperspectral camera and a multiprocessor computer system. Preliminary results targeted at the feasibility of the tool show that reasonable accuracy can be maintained in the real time requirements. The described project supports the further development of hyperspectral imaging as a general tool in remote sensing.