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This paper reports on preliminary results from a study applying the technique of spectral mixture analysis (SMA) to the measurement of temporal changes in the composition of urban morphology in the metropolitan area of Greater Cairo, Egypt, between 1987 and 1998. Although several remote sensing techniques have been used successfully for urban change analysis, most of these focus on change ‘between’ classes measured in a discrete, crisp way through which each pixel is assigned to a label indicating either a change or no change in the class to which the pixel originally belonged. In many major cities, such as Cairo, change also occurs within classes (e.g. vertical growth of buildings, increase in housing density, decrease in open spaces) and is reflected by an aggregation of land cover and urban materials. None of these materials may seem important in isolation. Rather, the significance of these urban land covers arises from the way they interweave with each other to structure the morphology of the urban place. In this paper, a ‘soft’ approach is presented to identify and measure the composition of changing morphology from multi-temporal, multi-spectral satellite images. SMA is demonstrated to be capable of deriving spatially continuous variables quantified at the sub-pixel level. These variables represent measures that can be compared across urban places and at different time periods. They can be integrated readily into a wide range of applications and models concerned with physical, economic and/or socio-demographic phenomena in the city