This paper proposes a probabilistic framework for classification of evolving targets, leveraging the principles of recursive Bayesian estimation in a perception-oriented context. By implementing a Gaussian toroid prediction model of the perception target's evolution, the proposed recursive Bayesian classification (RBC) scheme provides probabilistically robust classification. Appropriate features are extracted from the target, which is then probabilistically represented in a belief space. This approach is capable of handling high-dimensional belief spaces, while simultaneously allowing for multi-Gaussian representation of belief without computational complexity that hinders real-time analysis. The proposed technique is validated over several parameter values by thousands of simulated experiments, where it is shown to outperform naıve classification when high observational uncertainty is present.