The use of hyperspectral sensors for geological, agricultural and other remote sensing applications is continually increasing. In addition to airborne sensors, there are now at least four hyperspectral satellite sensors under development. These sensors will be producing a near continual stream of high dimensional data, leading to an obvious analysis bottleneck. Much of the planned analysis of hyperspectral image cubes requires the determination of certain basis spectra called 'end-members.' Once these spectra are found, the image cube can be 'unmixed' into fractional abundances of each material in each pixel. There exist several techniques for accomplishing the determination of these end-members, most of which require the intervention of a trained geologist. This process and the associated computations are often time- consuming. There is a need for automated techniques to allow the quick review of data collected by the sensors. Several different approaches to finding end-members in data will be reviewed, including the Pixel Purity Index, Orasis, and the Iterative Error Estimation methods. A new method, called N- FINDR, which extracts end-members based upon the geometry of convex sets, will be discussed in detail. End-member spectra and abundance maps will be compared to USGS results on AVIRIS data. Data examples from AVIRIS will also be used to compare several of the algorithms.