This paper addresses a new type of spatial queries, called the view field nearest neighbor (VFNN) query. Given a user's location (i.e., query point) and a user's view field (i.e., query view field), VFNN query finds the nearest data object that falls within the user's view field. To support efficient processing of VFNN queries, we utilize the R*-tree, one of the representative multi-dimensional index structures, to index a dataset. We propose the VFNN search algorithm on the R*-tree, which employs (i) a mindist to measure the minimum possible distance from a query point to each data object and (ii) a minangle (maxangle), the minimum (maximum) angle (viewed from the positive x-axis) between the query point and the minimum bounding rectangle (MBR) of each R*-tree node. Through a series of simulations, we study the performance of the proposed search algorithm.