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The identification of tea varieties and producing areas has an increasingly important market value as the continuous development of the tea industry. A method combining laser induced breakdown spectroscopy (LIBS) and neural network algorithm is proposed in order to identify tea varieties and producing areas rapidly and accurately. In this paper, LIBS spectra of six major tea varieties and eight green tea samples from different producing areas in the range of 200-430 nm are collected, elemental analysis and spectra pre-processing are performed, principal component analysis (PCA) method is applied to select the features, and error back propagation (BP) neural network is used to model the tea classification problem. Key issues such as feature dimensions, network parameters, network structure, and the size of dataset are discussed. The result shows that the best model accuracy reaches 100%, indicating that …