The spontaneous transitions between D-dimensional spatial maps in an attractor neural network are studied. Two scenarios for the transition from one map to another are found, depending on the level of noise: (i) through a mixed state, partly localized in both maps around positions where the maps are most similar, and (ii) through a weakly localized state in one of the two maps, followed by a condensation in the arrival map. Our predictions are confirmed by numerical simulations and qualitatively compared to recent recordings of hippocampal place cells during quick-environment-changing experiments in rats.