This paper explores the pairing between sensors and controllers to allow autonomous navigation in unknown environments. Addressing this problem by directly using all available sensor information in a controller (e.g. a neural network) is tempting, but problematic. If the sensor provides too little useful information, the controller selection and training will be both difficult and unlikely to lead to good system behavior. If on the other hand, the sensor provides too much information, the controller will be overwhelmed and lead to unnecessarily complex and brittle control laws. Ideally, the sensors should provide as much or as little information as is likely to be needed and used by the controller. In this work, we focus on this particular problem and analyze how different sensor suites can be designed and paired with controllers to provide optimum information for successful navigation. We explore ultrasonic and thermal sensors, and pair them with feed forward neural networks. The results show that neural networks trained via supervised methods provide good sensor interpretation (e.g., less than 11% testing error), but that for the simple controller used, the pairing of two sensor types increases overall error rather than reduce it, highlighting the difficulty in sensor∕controller matching.