We propose to use the Approximate Maximum-Likelihood (AML) method to estimate the direction-of-arrival (DOA) of multiple targets from various spatially distributed sub-arrays, with each sub-array having multiple acoustical/seismic sensors. Localization of the targets can with possibly some ambiguity be obtained from the cross bearings of the sub-arrays. Spectra from the AML-DOA estimation of the target can be used for classification as well as possibly to resolve the ambiguity in the localization process. We use the Support Vector Machine (SVM) supervised learning method to perform the target classification based on the estimated target spectra. The SVM method extends in a robust manner to the nonseparable data case. In the learning phase, classifier hyperplanes are generated off-line via a primal-dual interior point method using the training data of each target spectra obtained from a single acoustical/seismic sensor. In the application phase, the classification process can be performed in real-time involving only a simple inner product of the classifier hyperplane with the AML-DOA estimated target spectra vector. Analysis based on Cramér-Rao bound (CRB) and simulated and measured data is used to illustrate the effectiveness of AML and SVM algorithms for wideband acoustical/seismic target DOA, localization, and classification.