In this paper the design of a novel high-speed, low-cost autonomous line following robot that combines human expert knowledge and experiential data extracted through neural network training has been proposed and implemented. The proposed autonomous robot overcomes the disadvantages of earlier designs like oscillations in motion, exception handling, high-cost and high-energy consumption. The robot uses a novel square-topology infrared sensor matrix that enables it to anticipate a turn by sensing the track ahead. Three different strategies to control high-speed line following robots are presented: a neural network based control strategy, a learning vector quantization based strategy and a reinforcement learning based strategy. The final design based on a hybrid neural network based control strategy smoothly followed the benchmark track with just 4 neurons and used a low-cost 8-bit microcontroller to implement the control system. © 2015 Elsevier Ltd