This paper address the use of Indices Combination with Supervision Classification methods to extract urban built-up areas, vegetation and water features from Landsat Thematic Mapper (TM7) imagery covering Dar es Salaam and Kisarawe peri-urban areas. The study uses three indices; Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI) to reduce the seven bands Landsat TM7 image into three thematic-oriented bands. Data correlation, spectral confusion and redundancy between original multispectral bands were significantly reduced upon application of the techniques. As a result, the spectral signatures of the three urban land-use classes are more distinguishable in the new composite image than in the original seven-band image since the spectral clusters of the classes are well separated. Through a supervised classification on the newly formed image, the urban built-up areas, vegetation and water features were finally extracted effectively; with the accuracy of 82.05 percent attained. The results show that the technique is effective and reliable and can be considered for use in other areas with similar characteristics.