The recognition of sign language gesture through image processing and Machine Learning has been widely studied in recent years. This article presents a dataset consisting of 2400 images of the static gestures of the Peruvian sign language alphabet, in addition to applying it to a hand gesture recognition system using low resolution cameras. For the gesture recognition, the Histogram Oriented Gradient feature descriptor was used, along with 4 classification algorithms. The results showed that Histogram Oriented Gradient, along with Support Vector Machine, got the best result with a 89.46% accuracy and the system was able to recognize the gestures with variations of translation, rotation and scale.