Convolutional Neural networks(CNN) are one of the most powerful tools in the present era of science. There has been a lot of research done to improve their performance and robustness while their internal working was left unexplored to much extent. They are often defined as black boxes that can map non-linear data effectively. This paper answers the question, 'How does a CNN look at an image?'. Visual results are also provided to strongly support the proposed method. The proposed algorithm exploits the basic math behind CNN to backtrack the important pixels. This is a generic approach which can be applied to any architecture of a neural network. This doesn't require any additional training or architectural changes. In literature, few attempts have been made to explain how learning happens in CNN internally, by exploiting the convolution filter maps. This is a simple algorithm as it does not involve any cost functions, filter exploitation, gradient calculations or probability scores. Further, we demonstrate that the proposed scheme can be used in some important computer vision tasks such as object detection, salient region proposal, etc. © 2019 IEEE.