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 the authors have developed a hierarchical neural network architecture which combines multiresolution pyramid processing with neural networks. Here the authors discuss the application of their hierarchical neural network architecture to the problem of detecting microcalcifications in digital mammograms. Microcalcifications are cues for breast tumors. 30% to 50% of breast carcinomas have microcalcifications visible in mammograms while 60% to 80% of all breast tumors eventually show microcalcifications via histology. Similar to the building/ATR problem, microcalcifications are generally very small point-like objects (>10 pixels in mammograms) which are hard to detect. Radiologists must often exploit other information in the imagery (e.g. location of blood vessels, ducts, etc.) in order to detect these microcalcifications. Here the authors examine how well their hierarchical neural network architecture learns and exploits contextual information in mammograms.