A hybrid feature extraction framework for handwritten numeric fields recognition is described. The numeric fields were extracted from binary images of credit card application forms. The images include identity numbers (ID) and phone numbers. The feature extraction framework utilizes a cascade of a Kohonen self-organizing feature map (SOM) and a set of non-linear filtering units. The goals of our feature extraction process are to provide reliable information to the recognition stage. The recognition stage uses the feature set as inputs to a multi-layer neural network. We present experimental results which demonstrate the ability to extract features automatically in handwritten digit recognition. Experiments were performed on a test data set from the CCL/ITRI Database which consists of over 90,390 handwritten numeric digits. Recognition rate of 98.6% is achieved on this database.