We present a machine learning technique aimed at detecting abrupt changes in a sequence of vectors. Our algorithm requires a Mercer kernel together with the corresponding feature space. A stationarity index is designed in the feature space, and consists of comparing two circles corresponding to two   -SV novelty detectors via a Fisher-like ratio. An abrupt change corresponds to a large distance between the circles centers (w.r.t. their radii). We show that the index can be computed in the input space, and simulation results show its efficiency in front of real data. 1