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A new approach to time frequency transform and pattern recognition of non-Stationary power signals is presented in this paper. In the proposed work Visual localization, detection and classification of non-stationary power signals are achieved using HS-Transform and automatic pattern recognition is carried out using fuzzy C-means based Genetic algorithm. Time frequency analysis and Feature extraction from the non- stationary power signals is done by HS-Transform. Once the feature vectors are extracted is used for pattern recognition of various non-Stationary signals. Various non-stationary power signals are processed through HS-transform with hyperbolic window to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using fuzzy C-means algorithm and finally the algorithm is extended using Genetic algorithm to refine the cluster centers. The average classification accuracy of the disturbances is 93.25% and 95% using fuzzy C-means and Genetic based fuzzy C-means algorithm, respectively.