With the ever-increasing utilization of imagery in scientific, industrial, civilian, and military applications, visual pattern recognition has been thriving as a research field and has become an essential enabling technology for many applications. In this chapter, we present a brain-inspired pattern recognition architecture that can easily be adapted to solve various real-world visual pattern recognition tasks. The architecture has the ability to extract visual features from images and classify them within the same network structure; in other words, it integrates the feature extraction stage with the classification stage, and both stages are optimized with respect to one another. The main processing unit for feature extraction is governed by a nonlinear biophysical mechanism known as shunting inhibition, which plays a significant role in visual information processing in the brain. Here, the proposed architecture is applied to four real-world visual pattern recognition problems; namely, handwritten digit recognition, texture segmentation, automatic face detection, and gender recognition. Experimental results demonstrate that the proposed architecture is very competitive with and sometimes outperforms existing state-of-the-art techniques for each application.