We present a vision based localization system for a mobile robot that uses a representative set of images obtained during an initial exploration of the environment. This set of images makes it possible to represent the environment as a partially markov decision process. The originality of this approach is the resulting data fusion process that uses both image matching and decision made by the robot in order to estimate the set of plausible position of the robot and the associated probabilities. Image matching or recognition is achieved using principal components analysis.