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Handwritten Signature Recognition Is An Important Behavioral Biometric Which Is Used For Numerous Identification And Authentication Applications. There Are Two Fundamental Methods Of Signature Recognition, On-line Or Off-line. On-line Recognition Is A Dynamic Form, Which Uses Parameters Like Writing Pace, Change In Stylus Direction And Number Of Pen Ups And Pen Downs During The Writing Of The Signature. Off-line Signature Recognition Is A Static Form Where A Signature Is Handled As An Image And The Author Of The Signature Is Predicted Based On The Features Of The Signature. The Current Method Of Off-line Signature Recognition Predominantly Employs Template Matching, Where A Test Image Is Compared With Multiple Specimen Images To Speculate The Author Of The Signature. This Takes Up A Lot Of Memory And Has A Higher Time Complexity. This Paper Proposes A Method Of Off-line Signature Recognition Using Convolution Neural Network. The Purpose Of This Paper Is To Obtain High Accuracy Multi-class Classification With A Few Training Signature Samples. Images Are Preprocessed To Isolate The Signature Pixels From The Background/noise Pixels Using A Series Of Image Processing Techniques. Initially, The System Is Trained With 27 Genuine Signatures Of 10 Different Authors Each. A Convolution Neural Network Is Used To Predict A Test Signature Belongs To Which Of The 10 Given Authors. Different Public Datasets Are Used To Demonstrate Effectiveness Of The Proposed Solution.