The thesis is a further contribution to the area of robotic person following that has been an on-going research at the Intelligent Robots and Systems Laboratory. The goal of this research was to enhance the vision based robotic person following with the accelerometer and gyroscope using a standard cell-phone device. The signals that are generated by the cell phone on the person while the person walks provide information about the number of steps and the direction of the motion of the person. These sensor values are sent through a personal wireless network to the robot computer. The received signals are process using various signal processing techniques including the Kalman filter and peak detection algorithms to extract the number of steps taken by the person and the direction of the motion of the person. The processed signals are then used by a fuzzy logic algorithm to determine the distance between the robot and the person, thus identifying the location of the person with respect to the robot. The robot then follows the person based on the estimated location of the person. The person is also identified by a vision system as complementary and secondary method for person detection. The vision-based system develops a number of morphological operations to detect the person and segment it from the scene and other objects in the image. The complete system has been implemented on a Segway robot platform, and a number of tests have been performed, these tests include walking of different persons and recording the distances travelled, the estimation of the person’s location by the system and its comparison with the actual recorded measurements. The results show that in majority of cases the system is successful in person following in simple environments. The thesis concludes by outlining a number of improvements that can be made to the system for more robust operation.