Modem machining industries demand for improved surface roughness from micro level to nano level along with increased tool life and reduced cutting temperature and force during machining. Therefore, the aim of this research work is focused on optimization of Minimum Quantity Lubrication (MQL) parameters using nano fluids in turning of AISI 4340. A study of effect of MQL parameters on the surface roughness of AISI 4340 was carried out using nano fluid such as Multi Walled Carbon Nano Tube (MWCNT). In the experiment conducted, four values of pressure, four values of flow rate and two types of nano fluids were used. The chemical composition of the work material was tested using arc spectrometer and verified to be of grade AISI 4340. The test pieces were turned on a CNC lathe machine under MQL mode using nano fluid with different levels of MQL parameters by using Taguchi L16 orthogonal array. The surface roughness of the machined surface was measured using surface measurement tester. Taguchi methodology was used to optimize MQL parameters. The results were analyzed using Analysis of Variance (ANOVA). From result analysis, it was shown that, cutting fluid (Nano fluid) played a major role in producing lower surface roughness followed by flow rate whereas pressure has least significance in producing lower surface roughness under MQL using nano coolant. It was observed that ethylene glycol with nano fluid (MQL1) showed lowest surface roughness as compared to water with nano fluid (MQL2). The optimum condition under MQL mode with nano fluid obtained as pressure (5 bar), flow rate (140 ml/hr.) and cutting fluid type 1. From result analysis it is also observed that, ethylene glycol as a base fluid with nano fluid is a most significant factor affecting surface roughness. The percentage error between experimental and predicted surface roughnesses is below +/- 10%. Thus, with proper selection of MQL parameters and nano coolant, it is possible to achieve good surface roughness, reduce tool wear while maintaining the cutting forces and temperatures at reasonable levels. (C) 2017 Elsevier Ltd. All rights reserved.