In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also presents a new technique to remove slope and slant from handwritten numeral string and to normalize the size of text images and classify with supervised learning methods. Experimental results on a database of 102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained on independent digits contained in the numeral string of digits includes both the skewed and slant data.