The American Sign Language (ASL) alpha-numeric character classification/recognition without using any aid (embedded sensor, color glove) is really difficult task. This paper describes a novel method to classify static sign by obtaining feature set based on DCT (Discrete Cosine Transform) and Regional properties of hand image. Feature set of size 1860×74 is later trained and tested using different classifiers like MLP, GFFNN, SVM. We have collected dataset (alpha numeric character) from 60 people including students of age 20-22 years and few elders aged between 25-38 who have performed 31 signs resulting in total dataset of 1860 signs. Out of this 90% dataset is used for training and 10% considered for Cross validation. We have got maximum classification accuracy as 86.16 % on CV dataset using GFF Neural Network.