Sensors are mainly associated in order to get benefits of their complementarity. Different kinds of advantages may be expected such as the ability to face a more important set of situations, the improvement of discrimination capacity or simply time saving. When analyzing a situation, the available sensors are most often used under conditions that include uncertainties at different levels. In this paper, belief functions theory, a mathematical toolbox which allows to represent both imprecision and uncertainty, is used to represent, manage and reason with such uncertainties (imprecise measurements, ambiguous observations in space or in time, incomplete or poorly defined prior knowledge). Practical examples on how to use this theoretical framework in detection-recognition problems are provided. They have nice properties like the possibility to quantify that none of the original hypothesis is supported, that the value of some 'likelihoods' are unknown, that we can accept an a priori belief that really represents total ignorance. Several applications where belief functions have been successfully applied for multisensor data fusion are finally presented.