The problem of mine-like object classification is of great interest for military applications. A standard procedure is to perform classification based on features extracted from the shadow of the image. However the classification depends up to a certain extent on the accuracy of the features. Alternative approaches such as template matching or working directly on the image have also been studied. However this may not be feasible as they are computationally expensive. In this paper an original method using a superellipse detection procedure to classify mine-like objects in side-scan sonar images is presented. Superellipses provide a compact and efficient way of representing different mine-like shapes. By simply varying the squareness of the function different shapes such as spheres, rhomboids and rectangles can be easily generated. Hence we propose a classification of the shape based on the squareness parameter. The first step in this procedure extracts the contour of the shadow given by an unsupervised Markovian segmentation algorithm. Afterwards a superellipse is fitted by minimising an appropriate metric. As the term being minimised is non-linear a closed form solution is not available. Hence the parameters of the superellipse are estimated by the Nelder-Mead simplex technique. The method has been tested and assessed on real side-scan sonar images, providing satisfactory results. We conclude this work discussing the feasibility of the superellipse fitting model for mine classification and other applications such as pipe modelling. Also further extensions of this work are outlined.