An important process in remote sensing is spectral unmixing which is used to obtain a set of species concentration maps known as abundance images. Linear pixel unmixing, also known as linear mixture modeling, assumes that the spectral signature of each pixel vector is the linear combination of a limited set of fundamental spectral components known as end-members. Thus end-member selection is the crucial first step in the spectral unmixing process. A conveniently parameterized method for determining the appropriate set of end-members for a given set of multispectral images is proposed. The end-members are obtained from a thematic map generated from a modified ISODATA clustering procedure that uses the spectral angle criterion, instead of the common Euclidean distance criterion. The centroids of the compact and well-populated clusters are selected as candidate end-members. The advantages of this technique over common mathematical and manual end-member selection techniques are, (1) the resulting end-members correspond to physically identifiable, and likely pure, species on the ground, (2) the residual error is relatively small, and (3) minimal human interaction time is required. The proposed spectral unmixing procedure was implemented in C and has been successfully applied to test imagery from various platforms including LANDSAT 5 MSS (79 m GSD) and NOAA's AVHRR (1.1 km GSD).