Human generated patterns like handwritten characters are found to be fuzzy in nature upto certain extent. This presented work proposes a fuzzy conceptual approach to classify Handwritten Arabic Numerals based on invariant moments features and the divisions of numeral image into several parts. The Moment invariants features are well known for independence of size, slant, orientation, translation and other variations of handwritten characters. A database, created by American University in Cairo, of 7000 samples of each number from 700 different writers is used. Each image is normalized to 40X40 pixel size. Seven central invariant moments are evaluated for each image and its parts by dividing it by three different ways, i.e. three feature groups. The algorithm is experimented for 500 samples of each numeral image and 161 features were evaluated corresponding to each image. The performance rate of the method is found to be 95.14%.