This article presents a new approach to derive fine-scale socioeconomic information of urban areas using very high resolution satellite data. The rationale behind the method is to use high resolution satellite data, capable of resolving urban morphology details, to derive a classification of the image. Thus, it is assumed that there is a relationship between the socioeconomic profile and the urban morphology of an area in terms of availability of green areas, sport facilities, private swimming pools or pavement conditions. The method is tested using a case study of Lima, Peru. Using a sample of ground data, a neural network classifier was applied to a pre-classified image in which entropy had been used to mask extensive, non-built up areas that would otherwise have inserted spurious information into the classifier. The result shows a high correlation (0.70 R2) when compared with validation data. The good performances also show that a physiographic satellite view of the city reflects the socioeconomic layout of their inhabitants, thus making remote sensing a complementary tool for social research and urban planning. While the parameterization of the problem may differ from one area to another, it is shown that an a priori choice of a few parameters may help to automatically characterize large areas in social terms, thus allowing social inequality and its evolution to be mapped in those areas with limited availability of data. In order to make the method widely applicable, the possibilities and limitations of applying the procedure to other large cities are discussed. © 2011 Taylor & Francis.