The purpose of image analysis as a special type of signal analysis is to obtain relevant information from the physical world for decision making, like in industrial inspection, verification, alarm generation, for taking actions, like control of production systems, vehicle control, or for data transformation, like 3D-reconstruction, image coding, image rectification, etc. Any type of image analysis involves data grouping and data interpretation. The classical approach is the bottom-up strategy which is very efficient but requires extra efforts to obtain a high image quality and is quite sensitive even to slight changes in the scene setup, noise, disturbances, illumination effects, etc. The current trend in image analysis is to cope with more complex situations, like time varying scenes, spatial scenes, where the image depends on the viewing direction, or cases where the quality in one single image is too low to permit the extraction of reliable information. In these situations image analysis requires support from other sensors at different viewing angles, recordings form different time instances or the exploitation of different physical principles for image generation, i.e. multisensor data and the systematic use of knowledge. More flexibility in the analysis method is obtained by combining the classical data driven bottom-up strategy with the expectation driven top-down strategy. These new approaches will be illustrated in the paper by three examples, the analysis-by-synthesis strategy, multisensory analysis and knowledge based analysis.