With the advances in technology, low cost and low footprint sensors are being used more and more commonly. Especially for military applications wireless sensor networks (WSN) have become an attractive solution as they have great use for avoiding deadly danger in combat. For military applications, classification of a target in a battlefield plays an important role. A wireless sensor node has the ability to sense the raw signal data in battlefield, extract the feature vectors from sensed signal and produce a local classification result using a classifier. Although only one sensor is sufficient to produce a classification result, decision fusion of the local classification results for a number of sensor nodes improves classification accuracy. In our approach, we propose fuzzy decision fusion methods for single target classification in a WSN environment. Our proposed fusion algorithms use fuzzy logic for selecting the most appropriate sensor nodes to be used for classification. Our algorithms provide better classification accuracy over some popular decision fusion algorithms.