This paper presents a description of a Bayesian neural network (BNN) automatic target recognition (ATR) algorithm specifically developed to efficiently exploit the correlated relationships between the joint dimensional distribution properties of the multiple features. A multidimensional clustering process is used to define a Parzen estimate which approximates the Bayesian a posteriori decision function, but without limiting constraints regarding multimodal or correlated density function forms in multidimensional feature space. The mathematical derivation is placed in the form of a multilayer neural network, including Kernel function variance adaptation to accommodate small training data sets, and Kernel function tail saturation for robust handling of multidimensional feature data not represented in the training data. The BNN is shown to be a hybrid Bayesian/neural net classifier. It has a robust Bayesian statistical classifier as an initial state, and a feedback learning structure which enables learning corrections localized only to the feature space in the vicinity of the error. In addition, a method is described for integrating scene context information, spatial constraints, and a priori probability information into the BNN classifier. Example uses of the BNN algorithm are discussed for recent programs which obtained multiple feature ATR improvements from using the BNN with multiple frequencies, polarizations, aspect angles, and 3D interferometric SAR features.