We have proposed a neural network that learns to control avoidance behaviors of a physical mobile robot through classical conditioning and operant conditioning. In this article we test whether our network can acquire second-order conditioning. During training we first associate the activation of the robot’s infrared sensors with collisions. Then, the activation of a visual sensor is repeatedly paired with the activation of the infrared sensors. Results show that the robot learns to elicit avoidance responses whenever the visual sensor becomes active.