We present a method for finding the stability regions within a set of genuine signatures and for selecting the most suitable one to be used for online signature verification. The definition of stability region builds upon motor learning and adaptation in handwriting generation, while their selection exploits both their ability to model signing habits and their effectiveness in capturing distinctive features. The stability regions represent the core of a signature verification system whose performance is evaluated on a standard benchmark.