Increasingly demanding military requirements and rapid technological advances are producing reconnaissance sensors with greater spatial, spectral and temporal resolution. This, with the benefits to be gained from deploying multiple sensors co-operatively, is resulting in a so-called data deluge, where recording systems, data-links, and exploitation systems struggle to cope with the required imagery throughput. This paper focuses on the exploitation stage and, in particular, the provision of cueing aids for Imagery Analysts (IAs), who need to integrate a variety of sources in order to gain situational awareness. These sources may include multi-source imagery and intelligence feeds, various types of mapping and collateral data, as well the need for the IAs to add their own expertise in military doctrine etc. This integration task is becoming increasingly difficult as the volume and diversity of the input increases. The first stage in many exploitation tasks is that of image registration. It facilitates change detection and many avenues of multi-source exploitation. Progress is reported on the automating this task, on its current performance characteristics, its integration into a potentially operational system, and hence on its expected utility. We also report on the development of an evolutionary architecture, 'ICARUS' in which feature detectors (or cuers) are constructed incrementally using a genetic algorithm that evolves simple sub-structures before combining, and further evolving them, to form more comprehensive and robust detectors. This approach is shown to help overcome the complexity limit that prevents many machine-learning algorithms from scaling up to the real world.