A new recording technique for Hopfield-type dynamic auto-associative memories is proposed. The new technique is based on the finite and exponentially convergent algorithm of Ho and Kashyap for the solution of a system of linear inequalities. Associative neural memories recorded with the proposed algorithm are shown to be superior to those recorded with the Hopfield outer-product and Kohonen generalized-inverse techniques. The new recording algorithm is characterized by high capacity, high convergence rates to stored memories and low convergence rates to false and oscillatory states. The issue of stable false and oscillatory states is raised, and it is shown that such states appear to have a direct Boolean logic relationship with the stored memories.