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A novel learning algorithm, named ratio rule, for association of multi-valued patterns in a recurrent neural network is proposed in this paper. The learning is performed based on the degree of similarity between the relative magnitudes of the output of each neuron with respect to that of all other neurons. The dynamics of the neural network functions as a line attractor as opposed to the common concept of point attractor. The limit of the convergence region around the line of attraction is defined based on the statistical characteristics of the input patterns. Theoretical analysis of the associativity of the network with the ratio rule confirms the authenticity of its learning ability. The performance of the ratio rule on associativity and convergence of the recurrent network is evaluated by conducting several experiments on face images. It is observed that the ratio rule is suitable for retention, reconstruction, and restoration of learned patterns with varying face expressions.