Surface Electromyographic (sEMG) signals have been exploited for almost a century, for various clinical and engineering applications. One of the most compelling and altruistic applications being, control of prosthetic devices. The study conducted here looks at the modeling of the force and sEMG signals, using nonlinear Hammerstein-Weiner System Identification techniques. This study involved modeling of sEMG and corresponding force data to establish a relation which can mimic the actual force characteristics for a few particular hand motions. Analysis of the sEMG signals, obtained from specific Motor Unit locations corresponding to the index, middle and ring finger, and the force data led to the following deductions; a) Each motor unit location has to be treated as a separate system, (i.e. extrapolation of models for different fingers cannot be done) b) Fatigue influences the Hammerstein-Wiener model parameters and any control algorithm for implementing the force regimen will have to be adaptive in nature to compensate for the changes in the sEMG signal and c) The results also manifest the importance of the design of the experiments that need to be adopted to comprehensively model sEMG and force.