Background/Objectives: In the field of software development, the diversity of programming languages increase dramatically with the increase in their complexity. This leads both programmers and researchers to develop and investigate automated tools to distinguish these programming languages. Different efforts were conducted to achieve this task using keywords of source codes of these programming languages. Therefore, instead of using keywords classification for recognition, this work is conducted to investigate the ability of detecting the pattern of a programming language characteristic by using simbrain toolkit of neural network and testing the ability of this toolkit to provide detailed analysable results. Methods/Statistical analysis: the method of achieving these objectives is by using backpropagation neural network via Simbrain toolkit based on pattern recognition methodology. Findings: The results show that Simbrain neural network of pattern recognition is able to identify and recognize the pattern of C++ programming language with high accuracy. It also shows the ability of Simbrain toolkit to represent the analysable results through percentage of certainty. Improvements/Applications: it can be noticed from the results the ability of Simbrain toolkit to provide useful platform for studying and analysing the complexity of backpropagation neural network model.

Shallaw Mohammed Ali."A Pattern recognition model of C++ programming language using artificial neural network via Simbrain toolkit ". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.6, Issue 10,pp.4-12, October - 2019, URL :https://ijcert.org/ems/ijcert_papers/V6I1002.pdf,

[14] 	J.-h. C. J.-y. S. F. H. Jing Li, “Brief Introduction of Back Propagation (BP) Neural,” springer, vol. 2, pp. 553-558, 2012. 
[15] 	K. T. RashmiAmardeep, “Training Feed forward Neural Network With Backpropogation Algorithm,” International Journal Of Engineering And Computer Science, vol. 6, no. 1, pp. 19860-19866, 2017. 
[16] 	K. O. ,. S. A. M. N. Mutasem Alsmadi, “Back Propagation Algorithm : The Best Algorithm Among the Multi-layer Perceptron Algorithm,” International Journal of Computer Science and Network Security, vol. 9, no. 4, pp. 378-383, 2009. 
[17] 	Y. Z. Alaeldin Suliman, “A Review on Back-Propagation Neural Networks in the Application of Remote Sensing Image Classification,” Journal of Earth Science and Engineering, vol. 5, pp. 52-65, 2015. 
[18] 	j. y. Zachary Tosi, “Simbrain 3.0: A flexible, visually-oriented neural network simulator,” Elsevier journal neural network, vol. 83, pp. 1-10, 2016. 
[19] 	W.-L. L. ,. L. L. Tung-Hsu Hou, “Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets,” Journal of Intelligent Manufacturing , vol. 14, no. 2, pp. 239-253, 2003.