In our study, we proposed a new Neuro-fuzzy classification technique. The inputs to the Neuro-fuzzy classification system were fuzzy by applying the membership function to the Gaussian curve. The proposed method considered a fuzzification matrix in which the input characteristics were associated with a degree of membership in different classes. Based on the value of the degree of membership, a characteristic would be attributed to a specific category or class. We applied our method to five benchmark datasets from the UCI machine language repository for classification. Our objective was to analyze the proposed method and, then, compare its performance with the Multilayer Perceptron Backpropagation Network (MLPBPN) algorithm in terms of different performance measures such as accuracy, mean square error, Kappa statistic, true positive rate , false positive rate, Precision, recall and F-measure. In every respect, the proposed method performed better than MLPBPN.ATA mining and has attracted many researchers and analysts in the information industry and research organizations in general in recent decades , due to the availability of large amounts of data and the imminent need to change that data into meaningful information and knowledge. The useful information and knowledge gained can be used for applications ranging from market investigation, customer loyalty and production control to evolutionary analysis and scientific exploration [1], [2]. Classification as an important data mining technique involves extracting interesting patterns that represent knowledge from large real-world databases. Such analysis can provide in-depth insight for better understanding of diverse large-scale databases. The study related to effective knowledge development is... halfway through the article...” McGraw-Hill, 1996.[8] PJ Werbos, “The roots of backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting,” New York, NY: John Wiley & Sons, 1994.[9] L.A. Zadeh “Fuzzy Sets.” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.[10] B. Liu, “Uncertain Theory: An Introduction to Its Axiomatic Foundations,” Berlin: Springer-Verlag, 2004.[11] D. Dubois and H. Prade, “Fuzzy Sets and Systems ”, Academic Press, New York, 1988.[12] K. Elis J.-S.R. Jang, C.-T. Sun and E. Mizutani, “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and artificial intelligence", Prentice Hall, USA, 1997.[13] C.-T. Lin and CS George Lee, "Fuzzy neural systems: a neuro-fuzzy synergism with intelligent systems", Prentice Hall, 1996.[14] D . Nauck, F. Klawonn and R. Kruse, “Fundamentals of neurofuzzy systems”, Wiley, Chichester, 1997.