Control has undergone several distinct phases of development. There are various ways that one can classify the different phases. An overriding issue has been the way that actuators, controllers, and sensors are interconnected. There has been a progression from direct mechanical interconnection to pneumatic and hydraulic interconnection to electrical connection and, finally networked and/or wireless connection. The last phase has opened up entirely new challenges for the control system design community. Standard control theory assumes perfect communications between plant and controllers. The advances in communications technology have, on the other hand, motivated the use of general purpose communication networks in control. One of the most important observations about networked control is that there exist additional degrees of freedom in the design relative to the classical (nonnetworked) situation. In particular, one needs to prepare signals prior to transmission over the network (via some form of coding) and it is then necessary to reinterpret the signals upon receipt (via some form of decoding). This new control architecture increases applicability and reduces cost when compared to hard-wired solutions. However, there are also drawbacks to the use of networked control architectures. For example, typical communication links are subject to data-rate limits, are prone to data-loss, and may experience random delays. Dealing with these issues goes beyond standard control theory and a new integrated approach, lying at the interface between communication and control theory, has emerged inside the control community. A unified viewpoint that addresses all aspects of NCSs is, as yet, unavailable. However, many interesting results have been obtained. Good survey papers are contained in the special issue. The aim of this chapter is to give a brief introduction to one particular viewpoint, namely the signal-to-noise ratio (SNR) approach to the design of NCSs. This approach is relatively simple. It is, basically, a method where the channel is replaced by an additive noise source where the associated variance appears as a degree of freedom in the design. It is thus readily understandable by practicing engineers. Notwithstanding the simplicity of the approach, it yields important insights into the design and performance of NCSs. An advantage of the SNR approach is that it leads to simple methods for designing networked control architectures based on linear time invariant filters and linear control design methodologies. It is thus immediately useful in practical situations. The emerging literature on networked control contains many deep insights, for example, the minimal average data-rate across a channel necessary to stabilize an unstable system. These results typically depend on complex design of appropriate controllers and coder/decoder pairs. Interestingly, some of these results can be formally rederived by using simple additive noise models. Indeed, it will be shown below that, under suitable assumptions on the control architectures and encoding/decoding policies, it is possible to understand the impact of either average data-rate limits, or data-dropouts, in the context of SNR constraints in an additive white-noise (AWN) channel. These results allow one to utilize SNR-related results to draw conclusions that are valid in a broader context.FONDECYT942FONDECYT