The lubrication and tension levels are two important parameters when running a chainsaw, both impacting the cutting performance and lifespan of the tool. An appropriate level of lubrication and tension leads to maximum performance of the chainsaw and direct benefits for the end-user. This thesis addresses the problem of detecting the lubrication status and the tension level using the information contained in the acoustic emissions captured in the guidebar of the chainsaw. Data was collected by running controlled experiments using an acoustic emissions sensor.Information was extracted from acoustic emissions by using a number of features computed on different frequency ranges.Three machine learning models were trained and evaluated on data corresponding to different combinations of lubrication status and tension levels. The models' performances were evaluated using the well-known metrics accuracy, precision, and recall. A pattern was found for each lubrication and tension setup, and the model that registered the highest performance was the Random Forest. The impact of temperature, guidebar, and chain on acoustic emissions is also analyzed. The detection of different lubrication levels using the information contained by acoustic signals is also addressed, the patterns in data being determined by computing features in the time and frequency domains. The analysis shows that the temperature does not have an impact when the running time is less than 10 minutes, and the chain has a bigger impact than the guidebar for the specific setup of the experiments. Moreover, a pattern dependent on the guidebar and chain combination correlated with the lubrication level was identified. The main contribution of this thesis consists of detecting a pattern representative of lubrication and tension setup in acoustic emission using a number of features computed in different frequency ranges.