In this paper, a novel hierarchical prototype-based (HP) approach for classification is proposed. This approach is able to perceive the data space and derive the multimodal distributions from streaming data at different levels of granularity in an online manner, based on which it further identifies meaningful prototypes to self-organize and self-evolve its hierarchical structure for classification. Thanks to the prototype-based nature, the system structure of the HP classifier is highly transparent, and its learning process is of “one pass” type and computationally lean. Its decision-making process follows the “nearest prototype” principle and is fully explainable. The proposed HP approach is capable of presenting the learned knowledge in an easy-to-interpret prototype-based hierarchical form to users, and is an attractive tool for solving large-scale, complex real-world problems. Numerical examples based on various benchmark problems justify the validity and effectiveness of the proposed concept and general principles.