This paper presents our work on end-to-end (E2E) system development for multiple array track in the CHiME-5 challenge 2018. In particular, we propose to use E2E Lattice Free Maximum Mutual Information (LF-MMI) for acoustic modeling. For front-end, Mel Frequency Spectral Coefficients (MFSC) and Power Normalized Spectral Coefficients (PNSC) features are used. We employ delay-and-sum beamformer for speech enhancement of training and development data. The Recurrent Neural Network Language Model (RNNLM) rescoring is also explored along with 3-gram language model. Our E2E LF-MMI Time Delay Neural Network (TDNN) system performed better than the E2E system provided in the challenge with an absolute reduction of 10.95 % in WER. The final system combination further reduces the WER to 78.63 %. Hence, our proposed system combination captures complementary information due to various E2E systems trained on full training data, beamformed data and using MFSC and PNSC features, respectively.