The problem of handwritten recognition considered to be very important problem because of its numerous applications and theoretical values in the domain of pattern recognition. In this research, models of Neural Networks are used to recognize written characters, applying Artificial Neural Network (ANN) of three types, which are: - Kohenen All Classes in One Network (ACON), Kohenen One Class in One Network(OCON), and Learning Vector Quantization (LVQ). The feature extraction process made use of Haar Wavelet Transformation to extract the parametric features of the handwritten characters. Also Geometrical features were also used to extract features (Moment and Complex Moment). The system was implemented using Visual Basic Language, database of 130 persons was established, 70 samples from the database were used for training, and the all 130 samples were used for testing the system. The efficiency of the system was tested using the Recognition Rate. The results show that the wavelet transformation with both Kohenen Learning Vector Quantization and Kohenen One Class One Network (OCON) achieves the highest recognition rate in which it scores 94.