CyclinPred is a SVM based prediction method to identify novel cyclins using various features of proteins. Cyclins are a class of eukaryotic proteins which are crucial in cell-cycle control of an organism, first identified in early 1980s that are characterized by their periodic accumulation during interphase followed by rapid degradation at each meiotic or mitotic division. The diverse nature of cyclins in terms of sequence as well as its functionality made its prediction as an important and challenging problem in the biological world. The Prediction of Novel Cyclins can provide deep insight into new key players of cell-cycle and its various functional aspects as a function of different life-cycles in different organisms. Hence, a new prediction protocol needed to be designed and evaluated which is independent of sequential or structural homology to proteins with known functions. Here, the features used in CyclinPred are: amino acid composition, dipeptide composition, secondary structure composition and PSSM composition profile of proteins were considered for the development of method. Hybrid modules were also designed using these protein features. Depending on comparative evaluation of all models, blind-test performance and various statistical measures, the best model was decided. Here, we were able to achieve the prediction accuracy of 92.14% for PSSM based model. The current SVM model can predict both groups of cyclins: G1/S cyclins and G2/M cyclins.