Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasibleusing conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is aphysiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However,developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirementssuch as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose anovel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying thepeaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, fallingtime and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be usedfor classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available forsuch study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposedfeature set shows very good performance with an overall accuracy of approximately 95%. Although the proposed featureset is effective, the significance of individual features varies greatly (validated using significance testing) which led usto perform weighted voting of features for classification by performing autoregressive modeling. Our experiments showthat the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. Theweighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPGsignals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible forimplementation on standalone devices.