Mathematical morphology is a theory introduced more than 40 years. Since then, it has been widely used in the fields of image analysis and processing, due to its ability to analyse spatial structures (most often by means of a neighbourhood called structuring element) in a non-linear framework. Its application to binary and greylevel images is straightforward, relying on the set theory or preferably the lattice theory. However, its extension to multivalued images (where each pixel is represented by a vector instead of a scalar) is not trivial and is still an open problem. Once such an extension is available, a large panel of problems may be addressed by morphological approaches: template matching, image segmentation, image description, etc. In my talk, I will give an overview of my research works on these topics and illustrate methodological achievements in various fields: remote sensing, astronomical imaging, and content-based image retrieval.