Hypertensive Retinopathy (HTR) is a disease caused by high blood pressure flowing into the retinal blood vessels, resulting in thickening of blood vessel walls and reducing blood flow in the retina. Complications arising from this disease are diverse and dangerous, ranging from retinal vein occlusion, eye nerve damage, even blindness. This paper proposed a system for hypertensive retinopathy detection by using Principal Component Analysis (PCA) dan Backpropagation Neural Network (BNN). Retinal image was taken from STARE database which separated into learning and testing data with a ratio 7 to 3. PCA as fundus-image-dimension-reduction method has successfully reduced fundus image raw data by 99.9% reduction, thus cutting computation load for neural network training. This paper presented Backpropagation Neural Network (BNN) as main classification algorithm which done by setting its parameters, learning and testing the data. So the model could classify retinal image into one of two classes, namely the normal retina and retina with high blood pressure, based on the BNN output result. The proposed model result showed that testing accuracy up to 86.36%.