Prediction of ribonucleic acid (RNA) secondary structure is an important task in bioinformatics. The RNA structure is known to influence its biological functionality. RNA secondary structure contains many substructures such as stems, loops and pseudoknots. The substructure pseudoknot occurs in several classes of RNAs, and plays a vital role in many biological processes. Prediction of pseudoknots in RNA is challenging and still an open research problem. Several computational methods based on dynamic programming, genetic algorithms, statistical models, etc., have been proposed with varying success. In this paper, we employ matched filtering approach to determine the RNA secondary structure containing pseudoknots. The central idea is to use a matched filter to identify the longest possible stem patterns in the base-pairing matrix of an RNA. The stem patterns obtained are then used to determine the locations of the other substructures such as loops and pseudoknots present in the RNA. Comparison of the prediction results, for RNA sequences derived from PseudoBase, illustrate the effectiveness and the accuracy of our proposed approach as compared to some of the existing popular RNA secondary structure prediction methods.