An important problem in image analysis is finding small objects in large images. The problem is challenging because (1) searching a large image is computationally expensive, and (2) small targets (on the order of a few pixels in size) have relatively few distinctive features which enable them to be distinguished from non-targets. To overcome these challenges we have developed a hierarchical neural network (HNN) architecture which combines multi-resolution pyramid processing with neural networks. The advantages of the architecture are: (1) both neural network training and testing can be done efficiently through coarse-to-fine techniques, and (2) such a system is capable of learning low-resolution contextual information to facilitate the detection of small target objects. We have applied this neural network architecture to two problems in which contextual information appears to be important for detecting small targets. The first problem is one of automatic target recognition (ATR), specifically the problem of detecting buildings in aerial photographs. The second problem focuses on a medical application, namely searching mammograms for microcalcifications, which are cues for breast cancer. Receiver operating characteristic (ROC) analysis suggests that the hierarchical architecture improves the detection accuracy for both the ATR and microcalcification detection problems, reducing false positive rates by a significant factor. In addition, we have examined the hidden units at various levels of the processing hierarchy and found what appears to be representations of road location (for the ATR example) and ductal/vasculature location (for mammography), both of which are in agreement with the contextual information used by humans to find these classes of targets. We conclude that this hierarchical neural network architecture is able to automatically extract contextual information in imagery and utilize it for target detection.