A new paradigm is introduced for solving the forward kinematics of general Stewart platforms in real-time. It consists of an off-line preprocessing phase and an online realtime evaluation phase. In the preprocessing phase, the platform leg (link) space is decomposed into cells, and a large set of data is generated for the platform position/orientation (pose), and their corresponding link lengths are computed using the known inverse kinematics. Due to the existence of multiple solutions (poses) for a particular link vector, a data classification technique is employed to identify various solutions. The classified data are used to find the parameters of a simple model that represents the forward kinematics within a cell. These parameters are stored in a lookup table. During the online phase, given the link lengths, the appropriate cell is identified, the model parameters are retrieved from the lookup table and the poses are computed. The proposed method is tested on a Stewart platform, and the accuracy and online times are presented to show the effectiveness of the proposed method for real-time applications.