Hyperspectral imaging provides the potential to derive sub-pixel material abundances. This has significant utility in the detection of sub-pixel targets or targets concealed under canopy. The linear mixture model describes spectral data in terms of a basis set of pure material spectra or endmembers. The success of such a model is dependent on the choice and number of endmembers used and the unmixing process. Endmember spectra may come from field or laboratory measurements, however, differences between sensors and changes in environmental conditions may mean that the measurement is not representative of the material as found in the scene. Alternatively, a number of algorithms exist to select spectra from the data directly, but these assume pure examples of the complete set of materials exist within the imagery. In either case, the chosen set of endmembers may not optimally describe the data in a linear mixing sense. In this paper some new methods for endmember selection are presented. These are evaluated on hyperspectral imagery and the results compared with those of a well-known automatic selection technique. Finally, an improved unmixing architecture is proposed which is self-consistent in terms of endmember selection and the unmixing process.