Weighted semblance can be used for improving the performance of the traditional semblance for specific datasets. We propose a novel approach for prestack velocity analysis using weighted semblance. The novelty comes from a different weighting criteria in which the local similarity between each trace and a reference trace is used. On one hand, low similarity corresponds to a noise point or a point indicating incorrect moveout, which should be given a small weight. On the other hand, high similarity corresponds to a point indicating correct moveout, which should be given a high weight. The proposed approach can also be effectively used for analyzing AVO anomalies with increased resolution compared with AB semblance. Both synthetic and field CMP gathers demonstrate higher resolution using the proposed approach. Applications of the proposed method on a prestack dataset further confirms that the stacked data using the similarity-weighted semblance can obtain better energy-focused events, which indicates a more precise velocity picking.