 The problem of combining potentially conflicting pieces of information does not arise only when a group of people needs to make a decision. Artificial intelligence also explores ways to aggregate conflicting sensors’ information, experts’ opinions or databases into a consistent one (Bloch et al. 2001). The combination of information coming from heterogeneous sensors improves the deficiencies of the individual sensors increasing the performances of a system. Examples are sensors of gesture recognition, screen rotation, and accelerometer in smart phones. Distributed databases may need to be accessed and managed at the same time to share data, for example, a hospital may need to access the data collected about patients by different units. Internet users can find ratings on products provided by people who have purchased and assessed them on different online platforms. The type of information to be combined can differ, and so its representation can be numerical or symbolic: numbers, linguistic values, statistical probability distributions, binary preferences, utility functions etc. Yet all examples mentioned above deal with the problem of merging items coming from heterogeneous sources and with the issue of managing conflicts. At a purely formal level, belief merging studies the fusion of independent and equally reliable sources of information expressed in propositional logic. As with judgment aggregation, belief merging addresses the problem of fusing several individual bases expressed in propositional logic into a consistent one. Given the structural similarity of the problems investigated by these two disciplines, exploring their connections can reveal how similar these formalisms really are. On a more practical level, the application of operators defined in belief merging to judgment aggregation problems lead to the definition of a wider class of aggregation operators for judgment aggregation, the so-called distance-based procedures.