Quality of lubrication oil will impact the performance of equipment.Lubrication oil properties are being monitored periodically to ensure the quality of the oil is always good.Currently, oil change activity is conducted in time-based manner based on engine manufacturer recommendation.Therefore, lubrication oil will be discarded even though it is still useful.The idea of this paper is to consider multiple variables to assess the quality of lubrication oil as a higher number of variables are expected to give a more accurate prediction.In this study, multiple regression and artificial neural network (ANN) model were compared by assessing the R squared value and prediction error when predicting lubrication oil age.Spearman�s correlation was applied to the lubrication oil analysis data to assess the relationship between lubrication oil age with oil analysis parameters and identify the parameters that are highly correlated with oil age.Total base number (TBN), oxidation, iron (Fe), lead (Pb) and zinc (Zn) were identified as parameters that were strongly correlated with oil age.Multiple regression and ANN were applied to predict the oil age using these parameters as the predictor variables.Both models were compared based on its R squared value and prediction error namely mean square error (MSE) and mean absolute deviation (MAD).Multiple regression presented a better prediction accuracy with higher R squared value of 0.9249 and lower prediction error.However, the P value for the model were more than 0.05 which may be due to the multicollinearity that exist between the independent variables.The R squared value for ANN is considerably high with value of 0.758, which proved its ability to predict the desired oil age. © 2023, Institute of Technology PETRONAS Sdn Bhd.