There are various methods to model the cutting processes and tool life in machining operations. However, there is not any unchallengeable agreement on a certain method among the researchers yet. To enhance the performance of the monitoring and prediction systems, three most common methods of tool condition monitoring including artificial neural network (ANN), fuzzy logic, and least square (LS) approaches are evaluated in this present study. To reach to this aim, an experimental setup was established using a CNC machine tool and SAE4140 material. Cutting parameters and cutting forces during the machining process were considered as the inputs of the models, and the tool flank wear was defined as the output of the models. The results of the developed models were then compared to each other as well as the measured results by applying the used experiments and the test experiments. Furthermore, the modeling was renewed again with a small size of experiments. Based on the results, the ANN method was confirmed to be as the most accurate model than the others. However, when a small size of experiments was applied for designing the models, fuzzy logic method represented the most precise and reliable results in relation to ANN and least square-based models. The simulation diagrams of the models were also created to be used as an online system in machine tool adaptive control.