This paper proposes a new method for classification of gases/odors called average slope multiplication (ASM) using dynamic characteristics of thick film gas sensor array. The instantaneous values of the extracted dynamic response/recovery plots for various test gases viz., LPG, CCl4, CO, and C3H7OH were correlated to its neighboring response plots by the use of proposed ASM technique. It has been demonstrated that the proposed method offers excellent results for classification of gases/odors using the dynamic responses of thick film gas sensor array. Principal component analysis (PCA) has been further used for data preprocessing and dimensionality reduction. The extracted raw data, the ASM transformed data, and PCA preprocessed ASM data were trained and tested using the back-propagation neural network (BPNN). The results thus obtained have been studied and presented here. Cross validation scheme was adopted for all analysis. The BPNN trained and tested with raw data showed 86% classification accuracy, whereas the raw data after PCA preprocessing showed 90% classification. The ASM data showed 97% classification accuracy while ASM data with PCA preprocessing showed the best results giving 100% classification accuracy with duly trained BPNN. We therefore report that superior identification of gases/odors can be obtained using the dynamic response of gas sensor array with the proposed ASM method.