The main aim for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x - y plane. This problem is of critical importance since it incorporates temporal characteristics often found in real-time applications. Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks. This paper presents a max-density covering learning algorithm based on constructive neural networks which is efficient in terms of the recognition rate and the speed of recognition. The results show that it is possible to solve the spiral problem instantaneously (up to 100% correct classification on the test set).