The authors present the application of an artificial neural network, Kohonen's self-organizing feature map, for the classification of power system states. This classifier maps vectors of an N-dimensional space to a two-dimensional neural net in a nonlinear way, preserving the topological order of the input vectors. Therefore, secure operating points-that is, vectors inside the boundaries of the secure domain-are mapped to a different region of the neural map than insecure operating points. The application of this classifier to power system security assessment is presented, and simulation results are discussed