Basal stem rot (BSR), caused by Ganoderma boninense is known as the most damaging disease in oil palm plantations in Southeast Asia. Ganoderma could reduce the productivity of oil palm plantations and potentially reduce the market value of palm oil in Malaysia. Early disease management of Ganoderma could prevent production losses and reduce the cost of plantation management. This study focuses on identifying the thermal properties of healthy and BSR-infected tree using a thermal imaging technique. Thermal images of canopy section of oil palm trees from healthy and BSR-infected trees were captured. The images were processed to extract pixel value representing thermal properties of the trees. These values were statistically analysed. Selected principal component scores were used in classification k-nearest neighbour (kNN) and Support Vector Machine (SVM) multivariate classification algorithms. The algorithms were tested to classify the thermal images into healthy and BSR-infected group. The results demonstrated that when average pixel value of trees were used, the SVM-based model resulted in the highest average overall classification accuracy of 89.2% for training set and 84.4% for test set. This verifies the potential of thermal imaging for BSR detection in oil palm trees.