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Password authentication is a common approach to system security. The conventional verification table approach has significant drawbacks. Recently, neural networks have been used for password authentication to overcome the shortcomings of traditional approaches. In neural network approaches to password authentication, no verification table is needed; rather, encrypted neural network weights are stored within the system. Existing layered neural network techniques have their limitations such as long training time and recall approximation. This study proposes the use of a Hopfield neural network technique for password authentication. In comparison to existing layered neural network techniques, the proposed method provides better accuracy and quicker response time to registration and password changes.