Handwritten Signature verification is an important personal identification. It is widely used in authorizing a cheque or legal documents. Signature verification is either online (dynamic) or offline (static). A machine learning, Haar Cascade Classifier (HCC) approach was introduced by Viola and Jones to achieve rapid object detection based on a boosted cascade of Haar-like features. Here, for the first time the HCC approach was applied for the handwritten signature recognition and verification. Two datasets were used, UTSig dataset for Persian writers which written from right to left and includes 8,280 images from 115 writers. GPDS synthetic Signature database for English writers which written from left to right. It contains data from 4,000 synthetic individuals. A classifier was created for each one of the writers’ signatures after applying the preprocessing phase. Each classifier was trained and tested using enormous number of signatures generated from applying artificial noises on the signature images.