Tire-road friction estimation is one of the most popular problems for the tire and vehicle industry. Accurate estimation of the tire-road friction leads to better performance of the traction and antilock braking system controllers, which reduces the number of accidents. Several researchers have worked in the field of friction estimation, and many tire models have been developed to predict the tire-road friction. In this article, an intelligent tire, which has an embedded accelerometer placed on the inner liner of the tire, is used to estimate the tire contact patch length parameter and normal load. To accomplish this, first, an existing tire testing trailer equipped with a force hub to measure tire forces and moments, a high-accuracy encoder to measure the angular velocity of the wheel, and VBOX, which is a global positioning system–based device, to estimate the longitudinal speed of the trailer was used. As a practical application for the normal load algorithm, a wheeled ground robot, which is equipped with several sensors, including an accelerometer and a flexible strain sensor inside the tire (used for terrain identification purposes), was designed and built. A set of algorithms was developed and used with the test data that were collected with both the trailer and the robot, and the contact patch length and the normal load were estimated. Also, the friction potential between the tire and the road was evaluated using a small ground robot.