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This paper presents a methodology of cross-wavelet transform aided Fischer linear discriminant analysis (FLDA)-based feature selection and classification for sensing simultaneous occurrence of multiple power quality disturbances. A linear support vector machine is used for classification of the extracted features as it suits well with FLDA. This scheme is implemented in a general purpose microcontroller as a standalone module and the performance of the standalone module for sensing simultaneous occurrence of multiple power quality disturbances is judged by both online and offline testing. Results show that the performance is comparable with the results reported in the literatures. Moreover, the scheme is immune to real life uncorrelated noises due to incorporation of cross spectrum analysis in the feature extraction phase. The present method is generic in nature and can be implemented for any other microcontrollerbased applications addressing topologically similar problems. IndexTerms-Fischer linear discriminant analysis, microcontroller, cross wavelet transform, multiple power quality disturbance, support vector machine, standalone module.