In this paper, we present an approach for taskspecificobject representation which facilitates transfer of graspknowledge from a known object to a novel one. Our representation encompasses: (a) several visual object properties,(b) object functionality and (c) task constrains in order to provide a suitable goal-directed grasp. We compare various features describing complementary object attributes to evaluate the balance between the discrimination and generalization properties of the representation. The experimental setup is a scene containing multiple objects. Individual object hypotheses are first detected, categorized and then used as the input to a grasp reasoning system that encodes the task information. Our approach not only allows to find objects in a real world scene that afford a desired task, but also to generate and successfully transfer task-based grasp within and across object categories.