The eigenvalues of the Neumann Laplacian are used to generate three different sets of features for shape recognition and classification in binary images. The generated features are rotation, translation, and size invariant and are shown to be tolerant of boundary deformation. The effectiveness of these features is demonstrated by using them to classify 5 types of computer generated and hand drawn shapes. The classification was done using 4 to 20 features fed to a simple feedforward neural network. Correct classification rates ranging from 94.4% to 100% were obtained on computer generated shapes and 67.5% to 95.5% on hand drawn shapes.