The non textual image recognition is one of the important aspects of the multimedia. The image recognition is also termed as the computer vision and is used in the various areas and devices such as robotics. The ANN (Artificial Neural Networks) is one of the great tools involved in the image recognition. So far, the ANN were used in applications involving the computer vision. Although the artificial neural networks based systems are distinguished for their ability to cope with problems in pattern recognition and computer vision in general, they are rather impotent when faced with the dimensionality of problems in the image understanding domain. That makes them unable to deal sufficiently with problems such as rotation, distortion, clutter and scale variances. In this paper, we are going to deal the image recognition with the technology known as the CANN (Cellular Associative Neural Networks).The main thing which makes the difference between the traditional neural networks and the CANN is the architecture, which resembles the cellular automata. It is used in the interconnection between the various networks. We are going to have a look at the CANN technology, with the learning process and the recognition of images and the process of analysis of one dimensional and two dimensional images.