The Multilingual Speech Processing is the field of speech technology in which the speech signal of multiple languages of a speaker has been analyzed to observe the effect of the language on the speech features. On the basis of observation, a Multilingual Speaker Identification system can be designed for identification of the speaker in multilingual environments. For present study Multilingual Speech Processing database of different speakers has been recorded in three Indian languages, i.e., Hindi, Marathi, and Rajasthani. The sentences consist of consonants, i.e., “Cha”, “Sha” and “Jha”. Total numbers of speakers involved are 30 including males and females. The basic features of the speech signal: Pitch and first three Formant F1, F2 and F3 are calculated through PRAAT software where as cepstral features like Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) has been extracted from MATLAB software. A model is proposed to identify the speaker by multi language speech signal of a speaker using MFCC, GFCC and combined features as acoustic features. For training and testing, it is performed using neural network function Resilient Back Propagation Algorithm and Radial Basis Functions and results are compared. In this experiment accuracy of multilingual speaker identification is 94.77% using BPA and 96.52% using RBF neural network.