Tensor voting (TV) is a method for inferring geometric structures from sparse, irregular and possibly noisy input. It was initially proposed by Guy and Medioni and has been applied to several computer vision applications. TV generates a dense output field in a domain by dispersing information associated with sparse input tokens. In 3-D this implies that a surface can be generated from a set of input data, giving tensor voting a potential application in surface modeling. We study the tensor voting methodology in a modeling context by implementing a simple 3-D modeling tool. The user creates a surface from a set of points and normals. The user may interact with these tokens in order to modify the surface. We describe the results of our investigation.