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A major problem with blind equalization algorithms based on the distribution matching principle is that they need a long time to accomplish their convergence. What affects convergence has not been discovered to date. the similarity between the least mean‐square (LMS) algorithm (widely used for adaptive equalization) and the blind algorithms is considered here. It is expected that the convergence of the blind algorithms is dependent on the condition number of the correlation matrix of the input sequence. Prefiltering methods, including coefficient‐fixed type and coefficient‐adaptive type, are derived for blind equalization. In these methods, the prefilters are realized by a prediction error filter, which has the ability to compensate for amplitude distortion induced by a channel. Since the prefilters have such ability, the blind equalizers to be cascaded with them are required only to compensate for phase distortion. As a result, the burden imposed on the blind equalizers is reduced. the prefilters output a near‐white sequence and lead to an improvement in the convergence of the blind algorithms, being degraded as the condition number is increased. the effective ness of the proposed methods is validated by computer simulations.