Data fusion is a process to combine information from multiple sources and sensors, which can be of the same type (homogeneous) or have different characteristics (heterogeneous). This technique makes it possible to improve the estimation of physical quantities observed and the environment in which they are acquired; the data obtained from the fusion are more accurate with respect to the evaluation of a single sensor, and the errors are, therefore, reduced. The rationale behind this theory is the capability of one source to compensate the error of another, offering advantages such as increased accuracy and failure resilience. Therefore, the performances of the systems that fuse multiple data coming from different sources are deemed to benefit from the heterogeneity and the diversity of the information involved.

Multimedia data can be either sensory (such as audio, video, RFID) and sensory (such as WWW resources, databases). Possible applications involve an increasing number of sectors. Just to mention a few examples, we can consider the benefits of the multi-sensor fusion for smart environments (use of audio, video, presence detectors), security (extension of space observed , control of sensitive areas), aviation (flight safety), in the automotive field (detection of pedestrians and road transportation, pre-crash), in process control. The fusion of different media may provide additional information and increase the accuracy of the general decision process.