Spatial or temporal reasoning is an important task for many applications in Artificial Intelligence, such as space scheduling, navigation of robots, etc. Several qualitative approaches have been proposed to represent spatial and temporal entities and their relations. These approaches consider the qualitative aspects of the space relations only, disregarding any quantitative measurement. In some applications, e.g. multi-agent systems, spatial or temporal information concerning a set of objects may be conflicting. This paper highlights the problem of merging spatial or temporal qualitative constraints networks. We propose a merging operator which, starting from a set of possibly conflicting qualitative constraints networks, returns a consistent set of spatial or temporal information representing the result of merging.