Lately, the amount of waste plastics including black plastics is getting more and more increasing. According as lots of plastics are widely used in various industrial fields. Under these circumstances, necessity for recycling of limited useful resources is getting more and more important gradually and research related to plastic sorting system is being largely required for plastic recycling. Plastic sorting system constructed currently by Near Infrared Ray(NIR) is being exploited to classify colored plastics besides black plastic. However, the classification of black plastics still remains a challenging issue, because of the absorption of infrared rays of NIR spectrometer for black plastics. Design methodology to identify black plastics in introduced. ATR FT-IR, Raman, and LIBS spectroscopies are used to carry out qualitative as well as quantitative analysis and also comparative studies for black plastics. For ATR FT-IR spectrometer, the spectra data of black plastics can be measured through the contact of interval gap between the spectrometer and plastic. Its measurement speed is faster compared to NIR spectrometer. ATR FT-IR spectrometer which is the contact type of interval gap, has difficulty in the on-line application. As the contactless type of interval gap, Raman spectrometer can measure the samples quickly, but its ensuing effect leads to the difficulty of data extraction due to lots of noises as well as the difficulty of application to on-line system. Therefore, LIBS spectrometer which is the contactless type, is used to effectively extract spectra data being applied in the on-line system. But, whenever the spectra data are measured in the same sample through spectrometer, the position of peak points of the characteristic spectra data are partially changed or shifted. Design methodology which takes into consideration for the changed or shifted spectra data are introduce in this study. The design method of determining input variables corresponding to data peak points based on the chemical characteristic lead to more reasonable and effective technique for improving the performance of FRBFNN and SVM classifiers. Moreover, in order to improve the identification performance, intelligent computing algorithms such as Principal Component Analysis(PCA), Fuzzy Transform(FT), Fuzzy Radial Basis Function Neural Networks(FRBFNN), Support vector machine classifiers(SVM) and Particle Swarm Optimization(PSO) are considered to analyze and classify some types of black plastics. In the preprocessing step for classifying some black plastics, the characteristic peak points are extracted and region corresponding to each characteristic peak point is taken into consideration. Here, as the preprocessing techniques, PCA and Fuzzy Transform algorithms are used for the dimension reduction of data. And FRBFNN and SVM are exploited as intelligent classifiers. FRBFNN classifier is considered as the powerful tool with the synthesis technologies of fuzzy theory and neural networks for the identification of black plastics. SVM classifier is used for comparative studies with FRBFNN classifier. In conclusion, the design methodology related to preprocessing techniques based FRBFNN classifier is demonstrated as competitive and preferred network architecture, as well as superb performance.