The acoustic emissions of a ground vehicle contain a wealth of information which can be used for vehicle classification, e.g. in the battlefield. However, features that are extracted from the acoustic measurements are time-varying and contain a lot of uncertainties, which makes the classification challenging. Since it is impossible to establish precise mathematical models to describe these variations and uncertainties contained in the features, we have applied fuzzy set and fuzzy logic theories to model and manage them, and have proposed three fuzzy logic rule-based classifier (FL-RBC) architectures -- non-hierarchical, hierarchical in parallel and hierarchical in series -- for the multi-category classification of ground vehicles. These FL-RBC architectures have been implemented based on both type-1 and type-2 fuzzy logic theories. We have also conducted experiments on our proposed FL-RBC architectures as well as on a Bayesian classifier to evaluate their performances. The experiments have shown that for this multi-category classification problem, (1) all FL-RBC architectures perform much better than the Bayesian classifier, (2) the type-2 FL-RBC architectures perform better than their competing type-1 implementations, (3) the type-2 non-hierarchical and hierarchical in series FL-RBC architectures perform the best, and (4) the performance of a classifier can be improved by incorporating decision fusion.