Different types of tubes have been used to improve cooling systems regarding the performance, cost and compactness. Corrugated tubes are tubes with their inner surface enhanced in these systems. One of the applications of machine learning, named as pattern classification, is often used to separate the human faces, voices, finger prints etc. In this study, it is used to separate the R134a data taken in-tube boiling process in smooth and enhanced tubes automatically. In other words, the developed numerical algorithms enabled artificial intelligence to predict the type of tubes having equivalent diameters used in the experiments. Systematical experiments, including saturation temperatures of 10,15 and 20 degrees C, mass fluxes of 200, 300 and 400 kg m(-2) s(-1) and heat fluxes of 20,25 and 30W m(-2), are carried out for the comparisons using smooth and 5 different types of corrugated tubes having various corrugation depths and helix angles. The boiling process in the test tubes has been measured with 300 data points, having 30 individual parameters (inputs) for varying tube types for the reduction process by the Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA). The classification success rates of the methods of Linear Discriminant Classifier (LDC), Quadratic Discriminant Classifier (QDC), Naive Bayes Classifier (NBC) and Minimum Mahalanobis Distance Classifier (MMDC) by each dimensional reduction and the total area occupying under receiver operating characteristic (ROC) curves are determined according to 3-fold cross validation method. NBC method has the highest classification success with the accuracy of 98.33% as a result of the reduction to 3 dimensions by LDA method. In addition to this, QDC method has the highest area under curve (AUC) with the value of 0.9994 according to the reduction to 3 dimensions by LDA method. Dependency analyses showed that the use of 8 dimensional experimental parameters as the most important input is enough to determine the type of test tubes with a high accuracy. (C) 2014 Elsevier Ltd. All rights reserved.