In sensor environments and moving robot applications, the position of an object is often known imprecisely because of measurement error and/or movement of the object. In this paper, we present query processing methods for spatial databases in which the position of the query object is imprecisely specified by a probability density function based on a Gaussian distribution. We define the notion of a probabilistic range query by extending the traditional notion of a spatial range query and present three strategies for query processing. Since the qualification probability evaluation of target objects requires numerical integration by a method such as the Monte Carlo method, reduction of the number of candidate objects that should be evaluated has a large impact on query performance. We compare three strategies and their combinations in terms of the experiments and evaluate their effectiveness.