Presents a method for computing the shape skeleton of planar objects in presence of noise occurring inside the image regions. Such noise may be due to poor control of lighting conditions, incorrect thresholding or image subsampling. Binary images of objects with such noise exhibit sparseness (lack of connectivity), within their image regions. Such non-contiguity may also be observed in thresholded images of objects which consist of regions having varying albedo. The problem of obtaining the skeletal description of sparse shapes is ill posed in the sense of conventional skeletonization techniques. We propose a skeletonization method which is based on obtaining the shape skeleton by evolving an approximation of the principal curve of the shape distribution. Our method is implemented as a batch mode Kohonen self-organizing map algorithm and involves iterating the following two steps: (1) Voronoi tessellation of the data, (2) kernel smoothing on the Voronoi centroids. Adjacency relationships between the Voronoi regions are obtained by computing a Delaunay triangulation of the centroids. The Voronoi centroids are connected by a minimum spanning tree after each iteration. The final shape skeleton is obtained by joining centroids which are disjoint in the spanning tree, but have adjacent Voronoi regions. The skeletal descriptions obtained with the method are invariant to translation, rotation, and scale changes of the shape. The potential of the method is demonstrated on industrial objects having varying shape complexity under different imaging conditions.