Generally, dynamic characteristics of time delays between process correlated variables are major concerns in the process control community. However, traditional delay time analysis methods have limited ability to deal with dynamics of time delays. In response to this problem, this paper proposes a dynamic time delay analysis (DTA) based on the technology of time series data mining, aiming at effectively estimating transfer time delays between process correlated variables. Employing dynamic sliding windows, dynamic time delays can be estimated offline by calculating similarities between correlated variables. Subsequently, through an additional correlation analysis between the time delay and process variables, main variables influencing the time delay can be obtained. By providing relevant trend variables, an improved fuzzy interpolation prediction method is suggested to estimate the transfer time delay between process correlated variables online. In addition, a DTA dynamic directed time graph is created by combining dynamic transfer time delays of mutually dependent variables. Finally, performances of the DTA method are tested through a numerical study and a distillation column simulation. (C) 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.