Information mining/knowledge discovery and the associated data management are changing the paradigms of user/data interaction by providing simpler and wider access to Earth Observation (EO) data archives. Today, EO data in general and images in particular are retrieved from archives based on such attributes as geographical location, time of acquisition and type of sensor, which provide no insight into the imageÕs actual information content. Experts then interpret the images to extract information using their own personal knowledge, and the service providers and users combine that extracted information with information from other disciplines in order to make or support decisions. In this scenario, the current offering, which is Ôdata setsÕ or ÔimageryÕ, does not match the customerÕs real need, which is for ÔinformationÕ. The information extraction process is too complex, too expensive and too dependent on user conjecture to be applied systematically over an adequate number of scenes. This hinders access to already available or new data (data accumulation rate is increasing), penalises large environmental-monitoring type projects, and might even leave critical phenomena totally undetected. Emerging technologies could now provide a breakthrough, permitting automatic or semi-automatic information mining supported by ÔintelligentÕ learning systems.