We often can read or recognize a letter or word contaminated by stains of ink, which partly occlude the letter. If the stains are completely erased and the occluded areas of the letter are changed to white, however, we usually have difficulty in recognizing the letter, which now have some missing parts. This paper proposes a hypothesis explaining why a pattern is easier to be recognized when the occluding objects are visible. A neural network model is constructed based on the hypothesis and is demonstrated that the model responds to occluded patterns in a similar way as human beings.The visual system extracts various visual features from the input pattern and then recognizes it. If the occluding objects are invisible, the visual system will have difficulty in distinguishing which features are relevant to the original pattern and which are newly generate by the occlusion. If the occluding objects are visible, however, the visual system can easily discriminate relevant from irrelevant features and recognize the occluded pattern correctly. The proposed model is an extended version of the neocognitron model. The activity of the feature-extracting S-cells whose receptive fields cover the occluding objects is suppressed in the lowest stage of the hierarchical network.