In general, all tasks that demand any type of parameter estimation from multiple sources can benefit from the use of data/information fusion methods. The terms information fusion and data fusion are typically employed as synonyms; but in some scenarios, the term data fusion is used for raw data (obtained directly from the sensors) and the term information fusion is employed to define already processed data. In this sense, the term information fusion implies a higher semantic level than data fusion. Other terms associated with data fusion that typically appear in the literature include decision fusion, data combination, data aggregation, multisensor data fusion, and sensor fusion. Researchers in this field agree that the most accepted definition of data fusion was provided by the Joint Directors of Laboratories (JDL) workshop : ‘A multi-level process dealing with the association, correlation, combination of data and information from single and multiple sources to achieve refined position, identify estimates and complete and timely assessments of situations, threats and their significance.’ Hall and Llinas provided the following well-known definition of data fusion: ‘data fusion techniques combine data from multiple sensors and related information from associated databases to achieve improved accuracy and more specific inferences than could be achieved by the use of a single sensor alone.’ Briefly, we can define data fusion as a combination of multiple sources to obtain improved information; in this context, improved information means less expensive, higher quality, or more relevant information. Data fusion techniques have been extensively employed on multisensor environments with the aim of fusing and aggregating data from different sensors; however, these techniques can also be applied to other domains, such as text processing.The goal of using data fusion inmultisensor environments is to obtain a lower detection error probability and a higher reliability by using data from multiple distributed sources. The available data fusion techniques can be classified into three nonexclusive categories: (i) data association, (ii) state estimation, and (iii) decision fusion. Because of the large number of published papers on data fusion, this paper does not aim to provide an exhaustive review of all of the studies; instead, the objective is to highlight the main steps that are involved in the data fusion framework and to review the most common techniques for each step. The remainder of this paper continues as follows. The next section provides various classification categories for data fusion techniques. Then, Section 3 describes the most common methods for data association tasks. Section 4 provides a review of techniques under the state estimation category. Next, the most common techniques for decision fusion are enumerated in Section 5. Finally, the conclusions obtained from reviewing the different methods are highlighted in Section 6. A Review of Data Fusion Techniques