In view of the large number of machining parameters that influence the system dynamics, experimental study is performed using the Boring Trepanning Association's (BTA) deep hole drilling process by varying the cutting parameters (workpiece rotational frequency, drilling feed rate, and tool usage), which are considered as input of the process. The specimens were then tested for roundness, which is considered as an output of the process. In this study, first order Sugeno-fuzzy models are designed by using the cutting parameters as input data and the roundness as output data. The relation between the input and the output is created to find the influence of the input parameters on the output surface quality in terms of roundness error. Hence, the best cutting condition in deep hole drilling is designated to improve the output. A scheme is recommended to precisely create the relationship between the different cutting parameters using subtractive clustering procedure based on the first order Sugeno fuzzy model. Minimum error model with lesser numbers of rules for roundness error is established by enumerative exploration of the clustering parameters. The resulted model with best clustering factors is then attuned by using adaptive neuro-fuzzy inference system (ANFIS).