Biometric-based technologies are being widely used for personal authentication and identification in access control and e-commerce applications. Finger knuckle print, a new hand based biometric trait has recently been utilized in verification and identification based systems. A finger knuckle print possesses a unique and highly distinctive pattern. In this paper, the high discrimination capability of correlation filters were employed for a finger knuckle print based recognition system. The correlation filters have important characteristics like shift invariance and distortion tolerance. A minimum average correlation energy correlation filter was designed for finger knuckle print verification. The performance of the designed filter was evaluated by calculating the peak to side lobe ratio, the false acceptance ratio, the false rejection ratio and the equal error rate. The computational experiments were done on a Matlab platform on the Poly U finger knuckle print database.