Sensors acquire data, and communicate this to an interested party. The arising coding problem is often split into two parts: First, the sensors compress their respective acquired signals, potentially applying the concepts of distributed source coding. Then, they communicate the compressed version to the interested party, the goal being not to make any errors. This coding paradigm is inspired by Shannon’s separation theorem for point-to-point communication, but it leads to suboptimal performance in general network topologies. The optimal performance for the general case is not known. In this paper, we propose an alternative coding paradigm based on joint source-channel coding. This coding paradigm permits to determine the optimal performance for a class of sensor networks, and shows how to achieve it. For sensor networks outside this class, we argue that the goal of the coding system could be to approach our condition for op- timal performance as closely as possible. This is supported by examples for which our coding paradigm significantly outperforms the traditional separation-based coding paradigm. In particular, for a Gaussian exam- ple considered in this paper, the distortion of the best coding scheme according to the separation paradigm decreases like 1/logM, while for our coding paradigm, it decreases like 1/M, where M is the total number of sensors.