Lost circulation is one of the frequent challenges encountered during the drilling of oil and gas wells. It is detrimental because it can not only increase non-productive time and operational cost but also lead to other safety hazards such as wellbore instability, pipe sticking, and blow out. However, selecting the most effective treatment may still be regarded as an ill-structured issue since it does not have a unique solution. Therefore, the objective of this study is to develop an expert system that can screen drilling operation parameters and drilling fluid characteristics required to diagnose the lost circulation problem correctly and suggest the most appropriate solution for the issue at hand. In the first step, field datasets were collected from 385 wells drilled in Southern Iraq from different fields. Then, fscaret package in R environment was applied to detect the importance and ranking of the input parameters that affect the lost circulation solution. The new models were developed to predict the lost circulation solution for vertical and deviated wells using artificial neural networks (ANNs) and support vector machine (SVM). The using of the machine learning methods could assist the drilling engineer to make an intelligent decision with proper corrective lost circulation treatment. (C) 2019 Elsevier Ltd. All rights reserved.