The automation of fault detection in material science is getting popular because of less cost and time. Steel plates fault detection is an important material science problem. Data mining techniques deal with data analysis of large data. Decision trees are very popular classifiers because of their simple structures and accuracy. A classifier ensemble is a set of classifiers whose individual decisions are combined in to classify new examples. Classifiers ensembles generally perform better than single classifier. In this paper, we show the application of decision tree ensembles for steel plates faults prediction. The results suggest that Random Subspace and AdaBoost.M1 are the best ensemble methods for steel plates faults prediction with prediction accuracy more than 80%. We also demonstrate that if insignificant features are removed from the datasets, the performance of the decision tree ensembles improve for steel plates faults prediction. The results suggest the future development of steel plate faults analysis tools by using decision tree ensembles.