This paper will review recent advances in the applications of artificial neural network technology to problems in automatic object recognition. The application of feedforward networks for segmentation, feature extraction, and classification of objects in Forward Looking Infrared (FLIR) and laser radar range scenes will be presented. Biologi- cally inspired Gabor functions will be shown to be a viable alternative to heuristic image processing techniques for segmentation. The use of local transforms, such as the Gabor transform, fed into a feedforward network is proposed as an architecture for neural based segmentation. Techniques for classification of segmented blobs will be reviewed along with neural network procedures for deter- mining relevant features. A brief review of previous work comparing neural network based classifiers to conventional Bayesian and k nearest-neighbor techniques will be presented. Results from testing several alternative learning al- gorithms for these neural network classifiers are presented. A technique for fusing information from multiple sensors using neural networks is presented. The theoretical relationship between a multilayer perceptron trained using back propagation for classification and the Bayes optimal discriminant function is explained.