Virtual metrology (VM) is drawing more and more attention in the recent research of wafer to wafer control for semiconductor manufacturing. Although many different approaches for VM have been proposed, the adaptiveness of these approaches in feature selection and model complexity selection is still less discussed and most of the current researches rely on the summary statistics of the trace signals. In this work, an adaptive methodology based on the group method of data handling (GMDH) type polynomial neural networks is proposed to address these issues. In the proposed methodology, the processes for model selection and feature selection are fully automatic, and enhanced model performance can be achieved by employing two new types of features. To show the effectiveness of the propose methodology, the dataset from prognostics and health management (PHM) data challenge 2016 is employed to predict the material removal rate for the chemical-mechanical planarization process in semiconductor fabrication. The validation results report improved accuracy in comparison with several candidate methods, and the successful application of the proposed method suggests that the proposed method can be an effective tool for the virtual metrology in semiconductor manufacturing. (C) 2017 Elsevier Ltd. All rights reserved.