In the area of surveillance and reconnaissance, it is necessary to have an adequate situation awareness to be able to react to a critical situation in time. A critical situation develops over time and space, and is indicated by specific characteristics. These characteristics have to be known, and they need to be perceived and identified before danger occurs. Additionally, maritime surveillance areas are generally large, and threats in this domain are often asymmetric and cannot easily be classified. Therefore, widely distributed information sources, automatic situation analysis, and the possibility to share information of interest are necessary. An approach that makes it possible to percept information about a specific situation is to gather data from different sensor platforms (above and under water) and sensor types (e.g., optical and infrared imagery, video, radar, sonar). To serve user needs, relevant information has to be extracted and integrated into an overall picture. Therefore, methods of data fusion and classification are necessary. As threats are not easily identified by pure object classification (e.g., pirates and fishermen are making use of simple boats), situation analysis methods take specific motion patterns as indicators for abnormal behaviour into account. To disseminate the results and to share them with a broader community, methods and means of data dissemination have to be defined. The paper describes an architecture that implements this approach. The architecture has been proved in EU and NATO projects, and combines civil and military operations.