This paper proposes a method for matching sidescan sonar images. The main objective of this work is to exploit block-matching principle and improve its performance by embedding a relevant guidance algorithm. Instead of carrying out the block-matching process on the whole input images, which takes a lot of time, an image segmentation step is introduced in order to support and guide it. Thus, the block-matching is only performed on similar regions from the two segmented images, where the potential for finding relevant pairs of blocks is high. This improved version is expected to take less time than the original one. In this work, textural features are extracted from both images and used for feeding the unsupervised segmentation step implemented through self-organizing neural networks, the Kohonen maps. Experimental results show the effectiveness of the proposed guidance method in terms of calculation time, without any matching quality loss when compared to the regular blockmatching algorithm.