This paper deals with the problem of estimating an appropriate hand posture to grasp an object, from 2D object’s visual cues in a many-to-many (objects,grasp) configuration. A statistical learning protocol implementing vector-valued regression is adopted for both classifying the most likely grasp type and estimating the hand posture. An extensive experimental evaluation on a publicly available dataset of visuo-motor data reports very promising results and encourages further investigations.