This paper is concerned with recognition of hand-written and/or printed multifont alpha-numeric Bengali characters. It is assumed that characters are present in an isolated fashion. In the present work characters have been represented in terms of the primitives and structural constraints between the primitives imposed by the junctions present in the characters. The primitives have been characterized on the basis of the significant curvature events like curvature maxima, curvature minima and inflexion points observed along their extent. Curvature properties have been extracted after thinning the smoothed character images and filtering the thinned images using a Gaussian kernel. The unknown samples are classified using a two-stage feed forward neural net based recognition scheme. Experimental results have established the effectiveness of the technique. © 1993.