Object grasping is one of the basic functions required for many manipulator tasks. In particular, the grasping of unknown objects is often a desired functionality in manipulator system applications ranging from space exploration to factory automation. Due to the amount of object and environment data typically required to execute an unknown object grasp, computer vision is the sensing modality of choice. This paper presents a method for the automatic determination of plausible grasp points on unknown objects using an eye-in-hand robotic system and active deformable contour models. The system finds potential grasp point pairs, ranks all the possible pairs based upon measurements taken from the contour, and executes a vision-guided grasp of the object using the highest ranked grasp point. The paper also presents initial experimental results.