The feasibility of distinguishing multiple type components of exo-atmospheric targets is demonstrated by applying the Time Delay Neural Network (TDNN) and the Adaptive Time-Delay Neural Network (ATNN). Exo-atmospheric targets are especially difficult to distinguish using currently available techniques because all target parts follow the same spatial trajectory. Thus, classification must be based on light sensors that record signal over time. Results have demonstrated that the trained neural networks are able to successfully identify warheads from other missile parts on a variety of simulated scenarios, including differing angles, fragmented pieces and tumbling. The network with adaptive time delays (theATNN) performs highly complex mapping on a limited set of training data and achieves better generalization to overall trends of situations compared to the TDNN. We also apply the theorem of Funahashi, Hornik et al and Stone-Weier- strass to state the general function approximation ability of the ATNN. The network is trained on additive noisy data and shows that it possesses robustness to environment variations.