Multiple-feature implementation enables a holistic approach towards pattern recognition procedure that takes into consideration all significant features, which represent a particular set of complex patterns, such as images and sensor readings. This intends to reduce the bias effect of selecting only a single feature for classification/recognition purposes. In this paper we demonstrate the effectiveness of this approach in comparison with some well-known multi-feature pattern recognition techniques. Our approach benefits from having a set of distributed computational networks working together, forming a distributed recognition network that alleviates the issue of scalability against increasing number of features to be considered. In addition, the use of our proposed single-cycle learning distributed pattern recognition algorithm shows a significant reduction in training samples to achieve comparable accuracy. © 2011 IEEE.