Change detection is important for an up-to-date GIS database. The ever improving spatial, spectral and temporal resolution of satellite imagery allows for reliable detection and characterization of even more details of the changed patterns with higher accuracy. The quality of registration of the involved imagery is the key factor that dictates the validity and the reliability of the change detection results. The fact that the change detection process usually involves multi-spectral and/or multi-resolution imagery captured at different times and from different sensors emphasises the issue of development of a robust registration procedure that can handle these types of images. This paper introduces a new approach for automated image registration based on a hierarchical image matching strategy. After feature point extraction, the method uses the similarity of the grey levels to find the candidates of the homologous points across the images. To increase success rate and reliability, and reduce computational complexity, a hierarchical image pyramid has been used. Matching then starts from the highest pyramid level with the results being the approximation of the subsequent lower level. The algorithm also uses contextual information to achieve locally consistent matches. The method has been implemented and tested using various remote sensing imagery including IKONOS and QuickBird data over test sites in Melbourne, Australia and Thimphu, Bhutan. The results are promising and reveal the potential for operational automated image registration in the process of change detection.