•Pathogenesis of pear infected by A. alternata was monitored by HSI.•HSI was successfully used for early identification of pear black spot disease.•Spectral angle mapper was used to identify and segment the fruit infected area.•SVM model had better classification accuracy for early detection of disease. Pathogenetic process monitoring and early detection of black spot disease caused by Alternaria alternata on pear fruit is still difficult, as it causes only minor changes in the appearance of the infected area during the early stages of infection. In this study, the potential of hyperspectral imaging (HSI) for monitoring the pathogenetic process and early detection of the disease on pear fruit was evaluated. Fresh Korla pears were inoculated with Alternaria alternata and hyperspectral images were acquired from infected and control samples. Spectral angle mapping was performed to segment the infected area from sound tissue, and to monitor the pathogenetic process of the disease. Support vector machine (SVM), k-nearest neighbor, and partial least squares discriminant analysis models were developed and evaluated for their ability to detect early onset of the disease. Results concluded that the SVM model with an overall accuracy of 97.5% was most suitable for the proposed HSI technique. This study is the first reported attempt to use HSI to monitor the pathogenetic process and detect the early symptom of the disease in pear fruit.