Multi-color infrared imaging missile-warning systems require real-time detection techniques that can process the wide instantaneous field of regard of focal plane array sensors with a low false alarm rate. Current technology applies classical statistical methods to this problem and ignores neural network techniques. Thus the research reported here is novel in that it investigates the use of radial basis function (RBF) neural networks to detect sub-pixel missile signatures. An RBF neural network is designed and trained to detect targets in two-color infrared imagery using a recently developed regression tree algorithm. Features are calculated for 3 by 3 pixel sub-images in each color band and concatenated into a vector as input to the network.