A novel method that recognizes on-line handwritten patterns (typical in pen-based computing applications) is proposed. The method combines the advantages of both global and local recognition methods, works in real-time, and avoids the use of statistical models that require extensive user data. It is also one of the first methods that handles collectively cursive words, and hand-drawn line figures. The proposed system achieves pattern recognition through the use of shape metamorphosis. It is based on the premise that if two shapes are similar they don't have to undergo a substantial metamorphosis process in order for one to assume the shape of the other. In other words, the "degree of morphing" becomes the primary matching criterion. The notion of the "degree of morphing" is quantified through an energy minimization approach. The potential of the method is highlighted by a set of experiments.