The problem of sensor integration and data fusion is addressed. We consider the problem of combining information from diversified sources in a coherent fashion. We assume that at the fusion, the information from various sensors may be available in different forms. For example, data from infrared (IR) sensors may be combined with range radar (RR) data, and further combined with visual images. In each case, the data and information from the different sensors are presented in a different format which may not be directly compatible for all sensors. Furthermore, the available information may be in the form of attributes and not dynamical measurements. A theory for sensor integration and data fusion that accommodates diversified sources of information is presented. Data (or, more generically, information) fusion may proceed at different levels, like the level of dynamics, the level of attributes, and the level of evidence. All different levels are considered and several practical examples of real world data fusion problems are discussed.