Channel equalization is a major method for re-ducing distortion and interference effects on a com-munication channel. In this paper, channel equaliza-tion using soft computing methods is attempted. To be more specific, Dynamic Fuzzy Neural Networks (DFNN) which combines fuzzy rules and neural net-works is adopted. The DFNN is functionally equiva-lent to a Takagi-Sugeno-Kang (TSK) fuzzy system possessing the learning ability of a Radial Basis Function (RBF) neural network. The hidden neurons (rules) of the DFNN can be added and pruned dy-namically during the training process based on the significance of each neuron to achieve a compact to-pology structure. Simulation studies demonstrate that the performance of the DFNN-based equalizer is superior to some other existing equalizers in terms of Bit Error Rate (BER).