A crucial aspect of the management of data-centric and pervasive networks is the design of proper self-organizing data-distribution approaches, including aggregation, collection, diffusion, and so on. In this paper we introduce the collective sort problem for network environments that aims at collecting and segregating data by similarity. Data is collected and segregated in localized areas of the network selected by an emergent process. A solution to the problem is analyzed for a coordination scenario featuring a grid-like distributed set of Linda tuple spaces and a set of sorting agents executing a probabilistic protocol resembling brood collection in ant colonies. Based on simulation, we show how patterns of data collection emerge in spite of the very basic observation and computation abilities of sorting agents.