Research on graph comprehension has been concerned with relatively low-level information extraction. However, laboratory studies often produce conflicting findings because real-world graph interpretation requires going beyond the data presentation to make inferences and solve problems. Furthermore, in real-world settings, graphical information is presented in the context of relevant prior knowledge. According to our model, knowledge-based graph comprehension involves an interaction of top-down and bottom up processes. Several types of knowledge are brought to bear on graphs: domain knowledge, graphical skills, and explanatory skills. During the initial processing, people chunk the visual features in the graphs. Nevertheless, prior knowledge guides the processing of visual features. We outline the key assumptions of this model and show how this model explains the extant data and generates testable predictions.