Abstarct: Today multifingered robotic hands have become of great interest in robotics due to their advantages over conventional grippers in tasks requiring dexterous manipulation. Robotichands are more complex in construction and require more sophisticated analysis to determine the grasping force to be exterted on an object without dropping or breaking the object. In this thesis a neural network is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory and Lasalle invariance principle to solve Multifingered Robotic Hands (MRH) problems. According to the Saddle point theorem, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the strictly convex quadratic optimization problem. By employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original optimization problem. Simulation results show that the proposed neural network is feasible and efficient.