In many mining engineering applications such as prospecting, development, production and grouting, diamond bit drilling is widely used due to high penetration rate, core recovery and its ability to drill with less deviation. It has been well known that the operational parameters of diamond bit drilling are closely related with rock mass strength properties. One of the most widely discussed subjects in drilling is the possibility of using diamond drill bit operational parameters for preliminary estimation of rock mass strength and deformability properties used in many mining engineering design projects. Once such rock properties are estimated, it will be possible to make tactical planning decisions as mining progresses. In this study two different techniques, multiple regression and adaptive neuro-fuzzy inference system (ANFIS) were used to develop the models for preliminary estimation of rock mass strength. The variables used in the models are widely known and recorded operational parameters of diamond bit drilling such as bit load, bit rotation and penetration rate. To develop the models, a database covering the rock properties and the machine operational parameters collected from seven different drill holes in Turkey was constructed. Results indicate that both regression and ANFIS-based models can successfully be used to predict the rock mass strength. Adaptive neuro-fuzzy inference model exhibits better performance according to statistical performance indicators. By means of the developed models it is possible to estimate the strength of rock mass during drilling operation, especially in weak and highly fractured rock masses. The estimated strength parameters can be related to further mining engineering applications such as the assessment of excavatability, blast design and even mine design studies.