Taking Harare metropolitan province in Zimbabwe as an example, we classified Landsat imagery (1984, 2002, 2008 and 2013) by using support vector machines (SVMs) and analyzed built-up and non-built-up changes. The overall classification accuracy for the four dates ranged from 89% to 95%, while the overall kappa varied from 86% to 93%. The results demonstrate that SVMs provide a cost-effective technique for mapping urban land use/cover by using mediumresolution satellite images such as Landsat. Based on land use/cover maps for 1984, 2002, 2008 and 2013, along with change analyses, built-up areas increased from 12.6% to 36.3% of the total land area, while non-built-up cover decreased from 87.3% to 63.4% between 1984 and 2013. The results revealed an urban growth process characterized by infill, extension and leapfrog developments. Given the dearth of spatial urban growth information in Harare metropolitan province, the land use/cover maps are valuable products that provide a synoptic view of built-up and non-built-up areas. Therefore, the land use/cover change maps could potentially assist decision-makers with up-to-date built-up and non-built-up information in order to guide strategic implementation of sustainable urban land use planning in Harare metropolitan province.