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Decision feedback equalizers are used in wireless and mobile communications to reduce the intersymbol interference that is caused by the time dispersive channel. Here, an adaptive decision feedback equalizer is presented with a new adaptive algorithm. The algorithm uses sign normalized block based least mean square algorithm, and achieves a significant reduction of computational complexity. The proposed algorithm yields good bit error rate performance over a reasonable signal to noise ratio. In this scheme the incoming data is partitioned into non overlapping blocks and the filtering operation has been performed in frequency domain with FFT(overlap and save method). In this the correction applied to the tap weight vector and it is normalized with respect to the squared Euclidean norm of the tap input vector at time n. The frequency domain representation facilitates, easier to choose step size with which the proposed algorithm convergent in the mean squared sense, whereas in the time domain it requires the information on the largest eigen value of the correlation matrix of the input sequence. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of bit error rate (BER) and convergence rate.