This paper investigates the efficiency of spectral screening as a tool for speedup in hyperspectral image processing. Spectral screening is a technique for reducing the hyperspectral data to a representative subset of spectra. The subset is formed such that any two spectra in it are dissimilar and, for any spectrum in the original image cube, there is a similar spectrum in the subset. The similarity can be described through various spectral distances and can be controlled by a threshold value. The spectral screening is improved by associating with each spectrum in the subset a weighing factor proportional to the number of spectra in the original image that are similar to it. Following its generation, the subset is used in further computations instead of the full data. The resulting processing mappings are then applied to the data. The investigation focused on the comparison between distance measures such as spectral angle and spectral correlation angle, in terms of efficiency of the results and speedup obtained when tested with Principal Component Analysis (PCA) and Independent Component Analysis (ICA), two processing techniques used when dealing with hyperspectral data. We also investigated the advantage of weighting versus non-weighting the spectral subset, and the optimum performance of the screening algorithm. The experiments were performed on HYDICE, Hyperion and AVIRIS data and validate the usefulness of spectral screening for data reduction. Preprocessing through spectral screening provides significant speedup to PCA and ICA without reduction in data accuracy.