This paper presents an immune network-based emergent pattern recognition method. The artificial immune network provides more flexible learning tools than neural networks and clustering technologies. With a neural network, a network structure has to be defined first. The immune network allows their components to change and learn patterns by changing the strength of connections between individual components. The presented computational model achieves emergent pattern recognition by dynamically constructing a network of feature vectors to represent the internal image of input data patterns. The immune network-based emergent pattern recognition approach has tested using a benchmark civil structure. The test result shows the feasibility of using the presented method for the emergent structural damage pattern recognition.