Interpreting tertiary structure of a protein has been a crucial task in the field of biosciences. This problem can be addressed by detecting protein folds which is considered as an intermediate step in the tertiary structure prediction. From the perspective of computational sciences, the protein fold recognition can be subdivided in two steps: 1) feature extraction of protein sequences, and 2) identifying extracted features using appropriate classifiers. These steps are important to accurately identify folds of a novel protein sequence. In order to fully characterize a protein sequence, the number of features required is large and sometimes even unmanageable. This high dimensionality of features is difficult to process using conventional classifiers. Therefore, it is a challenge to develop and apply dimensionality reduction techniques for protein fold recognition. In this paper, we have emphasized the importance of dimensionality reduction techniques (DRTs) for protein fold recognition. To narrate, we have compared the recognition performance without DRT and with DRT on 3 benchmark datasets.