Iron resources have become very important in the global economy as they provide the inputs for the steel industry. The rapid industrial development of countries such as China, India, Brazil and other emerging economies in Asia and Africa have led to an increasing demand for steel products. This has resulted to an increase in global iron ore exploration. Located in the world’s biggest underdeveloped iron ore province (West Africa), Sierra Leone is correspondingly home to Africa’s largest iron ore (magnetite) deposit at present. The Tonkolili iron field in northern Sierra Leone forms the focus of this study. The primary iron mineralization here is magnetite. At the laterite and saprolite horizons have developed a supergene iron-oxide enrichment blanket which covers almost the entire area. Iron-oxides show diagnostic spectral features in the visible near infra-red (VNIR) portions of the electromagnetic spectrum which allows their remote identification.　　In this study, Landsat 7 Enhanced Thematic Mapper plus (ETM+) data are applied in modeling and targeting iron resources in Tonkolili. The key objectives are the mapping of banded iron formation (BIFs), discrimination of alteration zones and targeting new areas for BIFs and haematite prospects in the Tonkolili iron field. The methods employed in ETM+ image enhancement are the RGB, Crosta, band ratios and false color composites. The results show that for the mapping of magnetite-BIF, the ETM 321 and 754 in RGB color composites produced the most spectrally unique colors. The principal component-based Crosta technique enhanced areas with iron-oxides and hydroxyl-bearing minerals. The band ratio technique was only effective in enhancing areas with iron-oxide minerals using band ratios 3/1 and 5/4. The modeled alteration anomalies were overlain with the simplified geological map of Tonkolili and iron-oxide anomalies were found to follow the NE-SW trending of magnetic anomalies. This trend points to the fact that oxidative-weathering of the surface magnetite-BIFs have resulted to these iron-oxide anomalies in the Tonkolili laterite duricrust and saprolite layers. In the final iron prospecting map, BIF boundaries were modified and five haematite prospects were inferred for field inspection. This study therefore concludes that, the application of remote sensing data such as ETM+could provide a fast and complementary tool in recognizing prospective areas of iron ore mineralization found in Archaean Greenstone terranes such as Tonkolili in Sierra Leone.