Hierarchical neural network approaches have been developed first for combining high and low frequency (HF and LF) Side Scan Sonar imagery, and then for the combination of both acoustic images and Magnetic data. The adopted acoustic data fusion approach consists in a image-screening/HF, LF blob matching stage, followed by an information fusion/classification stage. Three variants of the information fusion/classification algorithm were conceived and evaluated based on `aggregate-feature-combining', `neural-network-discriminant-combining', and individual classifier `decision-based-combining', respectively. The `discriminant- combining' case yielded the best classification performance, and when compared with individual HF, LF classifier performance resulted in at least an order of magnitude reduction in the density of false alarms. Next, results are obtained for combining both acoustic and magnetic data using the described high and low frequency side scan sonar discriminant combining fusion algorithm as a starting point. In the next step, acoustic image pair `tokens' are associated with magnetic `tokens', resulting in three classes of resulting `tokens': `associated' acoustic-pair and magnetic tokens, isolated acoustic-pair tokens, and isolated magnetic `tokens'. Neural network output discriminants are derived for each of the three types of tokens mentioned above, and are employed to make classification decisions. The resulting Detection/Classification Algorithm is evaluated based on a combined ground truth obtained from both acoustic and magnetic sources.