The main challenge of designing classification algorithms for sensor networks is the lack of labeled sensory data, due to the high cost of manual labeling in the harsh locales where a sensor network is normally deployed. Moreover, delivering all the sensory data to the sink would cost enormous energy. Therefore, although some classification techniques can deal with limited label information, they cannot be directly applied to sensor networks since they are designed for centralized databases. To address these challenges, we propose a hierarchical aggregate classification (HAC) protocol which can reduce the amount of data sent by each node while achieving accurate classification in the face of insufficient label information. In this protocol, each sensor node locally makes cluster analysis and forwards only its decision to the parent node. The decisions are aggregated along the tree, and eventually the global agreement is achieved at the sink node. In addition, to control the tradeoff between the communication energy and the classification accuracy, we design an extended version of HAC, called the constrained hierarchical aggregate classification (cHAC) protocol. cHAC can achieve more accurate classification results compared with HAC, at the cost of more energy consumption. The advantages of our schemes are demonstrated through the experiments on not only synthetic data but also a real testbed.