Object segmentation in multidimensional data spaces is a pivotal component of modern computational analysis. Frequently, accurate segmentation hinges on the detection and localisation of object boundaries. The targeted objects often exhibit spherical symmetry. This paper introduces an algorithm for the automatic detection of n-dimensional hyperspheres embedded in (n+1)-dimensional Euclidean space. The algorithm utilises an evolutionary computation strategy to estimate hypersphere parameters from extensive point clouds. This method demonstrates notable advantages over traditional approaches such as the Hough transform and active surface models. Preliminary results suggest strong potential of the proposed technique as well as its adaptability to broader classes of hypersurfaces, offering a promising extension for future exploration.