The focus of the present work is natural human like grasping, for realistic performance simulations in digital human modelling (DHM) environment. The performance simulation for grasping in DHM is typically done through high level commands to the digital human models (DHMs). This calls for a natural and unambiguous scheme to describe a grasp which would implicitly accommodate variations due to the hand form, object form and hand kinematics. A novel relational description scheme is developed towards this purpose. The grasp is modelled as a spatio-temporal relationship between the patches (a closed region on the surface) in the hand and the object. The task dependency of the grasp affects only the choice of the relevant patches. Thus, the present scheme of grasp description enables a human like grasp description possible. Grasping can be simulated either in an interactive command mode as discussed above or in an autonomous mode. In the autonomous mode the patches have to be computed. It is done using a psychological concept, of affordance. This scheme is employed to select a tool from a set of tools. Various types of grasps a user may adopt while grasping a spanner for manipulating a nut is simulated. Grasping of objects by human evolves through distinct naturally occurring phases, such as re-oreintation, transport and preshape. Hand is taken to the object ballpark using a novel concept of virtual object. Before contact establishment hand achieves the shape similar to the global shape of the object, called preshaping. Various hand preshape strategies are simulating using an optimization scheme. Since the focus of the present work is human like grasping, the mechanism which drives the DHMs should also be anatomically pertinent. A methodology is developed wherein the hand-object contact establishment is done based on the anatomical observation of logarithmic spiral pattern during finger flexion. The effect of slip in presence of friction has been studied for 2D and 3D object grasping endeavours and a computational generation of the slip locus is done. The in-grasp slip studies are also done which simulates the finger and object response to slip. It is desirable that the grasping performance simulations be validated for diverse hands that people have. In the absence of an available database of articulated bio-fidelic digital hands, this work develops a semi-automatic methodology for developing subject specific hand models from a single pose 3D laser scan of the subject's hand. The methodology is based on the clinical evidence that creases and joint locations on human hand are strongly correlated. The hand scan is segmented into palm, wrist and phalanges, both manually and computationally. The computational segmentation is based on the crease markings in the hand scan, which is identified by explicitly painting them using a mesh processing software by the user. Joint locations are computed on this segmented hand. A 24 dof kinematic structure is automatically embedded into the hand scan. The joint axes are computed using a novel palm plane normal concept. The computed joint axes are rectified using the convergence, and intra-finger constraints. The methodology is significantly tolerant to the noise in the scan and the pose of the hand. With the proposed methodology articulated, realistic, custom hand models can be generated. Thus, the reported work presents a geometric framework for comprehensive simulation of grasping performance in a DHM environment.