Much of commonsense knowledge in real world is the form of procudures or sequences of steps to achieve particular goals. In recent years, knowledge extraction on procedural documents has attracted considerable attention. However, they often focus on procedural text but ignore a common multimodal scenario in the real world. Images and text can complement each other semantically, alleviating the semantic ambiguity suffered in text-only modality. Motivated by these, in this paper, we explore a problem of grounded multimodal procedural entity recognition (GMPER), aiming to detect the entity and the corresponding bounding box groundings in image (i.e., visual entities). A new dataset (Wiki-GMPER) is bult and extensive experiments are conducted to evaluate the effectiveness of our proposed model.