The problem of classifying regions in an IR cloud scene is considered using the conventional matched filter, the perceptron, and a class of neural networks known as the multilayered perception (MLP). The purpose is to determine if neural network technology is applicable for a remote-sensing application. Another goal is to illustrate graphically how the MLP generates the decision surfaces. Examples are given to show that the MLP is better than other approaches if the application requires nonlinear decision surfaces for classification. A three-dimensional classification problem is given to illustrate the MLP hidden-layer operation. The resulting nonlinear decision surface, which achieves 100% correct classification, is displayed. A seven-dimensional classification problem is given to further illustrate the need for a nonlinear decision surface. Finally, an 128 x 128-pixel IR cloud-scene example with remote objects provides a more practical indication of the benefits of MLP for point-object classification. In addition, these examples provide a tutorial on how the internal operations of the MLP produce the required decision surfaces. The results provide a rationale for consideration of the MLP for other remote-sensing applications.