Abstarct: Acid is injected into wells for stimulating oil and gas producing laxyers by dissolving some parts of the formation and creating new flowing channels to produce oil and gas (carbonate formations) or by opening the paths which were damaged or blocked (sandstone formations). The goals of acidizing oil and gas production wells are: opening pores,increasing production, increasing permeability, cleaning and soaking, cleaning tubes, releasing drilling strings and generally to decrease the skin value. Considering complexity of this operation and itʼs dependency to the diferent factors it is hard to determine these factors in an exact manner. Using optimization methods with quick performance and without need to complex assumptions for calculation of acidizing design parameters such as acid injection rate and acid volume will have good effects on increasing production rate of the oil and gas wells. This study aims to provide a method to improve the acidizing time and cost and at the same time increase the well productivity. Artificial neural networks are biologically inspired computing method which has an ability to learn; self-adjusted and are trained, capable of classification, image processing and different problem analysis, with an attempt to estimate. This case study presents the features and frxamework for application of neural network in optimizing acidizing design parameters in one of iran oil fields, using well and reservoir primary data. The results of this study can be sumorized as; since simulation method needs a lot of assumptions and data it overestimate the volume of acid required, so the cost and duration of operation increses while artifitial neural network suggests practical and better results