An estimation algorithm for vehicle driving load has been proposed in this paper. Driving load is an important factor in a vehicle's longitudinal motion control. An approach using an observer is introduced to estimate driving load based on inexpensive RPM sensors currently being used in production vehicles. Also, the new torque estimation technique using neural network has been incorporated in this estimation algorithm to achieve better performance over variations in the automotive power transmissions process. The effectiveness of the observer-based method is demonstrated through the use of a nonlinear full vehicle simulation model in various scenarios. The proposed method using an observer has good performance, both over modeling error in powertrain system and under the uncertain environment of a running vehicle. An accurate estimate of the driving load can be very helpful in the development of the advanced longitudinal control systems such as intelligent cruise control systems, CW/CA systems, and platoon control systems.