Nowadays laser machining became an attractive machining process for difficult to cut materials like ceramics, composites and super alloys. Titanium alloys specially Ti-6Al-4V (grade 5) is most widely used for different technologically advanced industries due to their superior performance characteristics such as high strength and stiffness at elevated temperatures, high strength to weight ratio, high corrosion resistance, fatigue resistance, and ability to withstand moderately high temperatures without creeping. Laser trepan drilling (LTD) being a thermal and non contact nature and having the ability to produce micro dimensions with required level of accuracy. However laser drilled holes are inherently associated with a number of defects like non circularity of hole, spatter thickness and hole taper. The present paper investigate the laser trepan drilling (LTD) process performance during trepanning of titanium alloy (Ti-6Al-4V) by modeling and simultaneous optimization of three important performance challenges such as hole taper (HT), circularity at entrance (CIRentry) and circularity at exit (CIRexit). A hybrid approach of artificial neural network (ANN)-genetic algorithm (GA) and grey relational analysis (GRA) has been proposed for multi-objective optimization. The verification results are in the close agreements with the optimization results.