This paper shows the feasibility of using an intelligent systems approach to increase the accuracy of a 3D ultrasonic position estimation system for real-time image guided neurosurgery. Current image guided systems use camera based technology that is space-intensive, have an accuracy of about 1.0–2.0 mm, and are prone to occasional failures. The 3D system presented in this paper eliminates the space intensive camera, has an accuracy of around 1.0 mm in the operating range of about 200–400 mm, makes the system independent of line-of-sight occlusion problems, and is expected to pave the way for accurate fusion models of MRI and ultrasonography to account for brain shifts during surgery. Hence, the proposed system provides many more advantages over existing systems without compromising on the accuracy. This paper presents the system formulation, a neural network model that uses the raw signals, the electronic hardware for data acquisition and processing as well as simulation and actual results.