We propose to use pattern‐guided dip estimation to mitigate aliasing problem that possibly exists in structure‐oriented data processing. A straightforward and effective approach of generating pattern‐guided dip is presented, which generally involves three rounds of standard dip estimation with plane‐wave destruction filters. The first use of plane‐wave destruction filter is for generating a mask operator distinguishing aliased and non‐aliased data, based on measuring the uncertainty of the first dip estimation. The second plane‐wave destruction filter uses the aliasing‐free portions of the input data, and the dip in the aliasing‐affected area is automatically padded with the ‘pattern’ dip by smoothing regularization. The result of the second plane‐wave destruction filter is used as the initial dip for the inversion of the last‐pass plane‐wave destruction filter, which produces a pattern‐guided dip. For some specific applications, the mask operator can be easily generated through other methods, and we can skip the first dip estimation. Two numerical examples, related to picking information using predictive painting and structure‐oriented interpolation, respectively, demonstrate that our pattern‐guided dip can effectively mitigate the aliasing problem in structure‐oriented data processing.