The performances of two image analysis methods for the classification of some randomly selected KCl crystals from a crystallization experiment into four two-dimensional classes (nearly circular, square, rectangular and irregular) are compared. The first method uses the first 15 Fourier descriptors of the angular bend as a function of are length of the periphery of the particles, whereas the second method is based on a combination of seven geometrical and morphological parameters of the crystals using a commercially available image analysis system (Visilog, Noesis, Orsay, France). The feedforward neural network with backpropagation learning algorithm and discriminant factorial analysis (STATlab, SLP, Ivry sur Seine, France) were found to classify the crystals with similar success.