A novel method employing a unique combination of wavelet based local singularity analysis and support vector machines (SVM) classification is described and illustrated by considering the case example of flow regime identification in gas-liquid stirred tank equipped with Rushton turbine. Pressure fluctuations time series data obtained at different operating conditions were first analyzed to obtain the distribution of local Hölder exponents' estimates. The relevant features from this distribution were then used as input data to the SVM classifier. Employing this method we could classify flow regimes with 98% accuracy. The results highlight the fact that the local scaling behavior of a given regime follows a distinct pattern. Further, the singularity features can be employed by intelligent machine learning based algorithms like SVM for successful online regime identification. The method can be readily applied to the other multiphase systems like bubble column, fluidized bed, etc. © 2005 Elsevier Ltd. All rights reserved.