Rapid detection and classification of bacteria colonies ( Escherichia coli, Listeria monocytogens and Staphylococcus aureus) were investigated by using hyperspectral imaging. The hyperspectral reflectance images (390-1040 nm ) of bacterial colonies on agar plates were collected. Bacterial spectra were extracted automatically based on the masks produced by segmenting a band difference image using the OTSU method. Full wavelength and simplified PLS-DA models were established for classification of bacterial colonies. For the full wavelength model, the overall correct classification rate ( OCCR) and confident OCCR for the prediction set were 100% and 95. 9%, respectively. Besides, competitive adaptive reweighted sampling ( CARS), genetic algorithm ( GA ) and least angle regression-least absolute shrinkage and selection operator ( LARS-Lasso) were used to select feature wavelengths for the development of simplified models. Among them, the CARS-model outperformed the other two in terms of precision, stability and classification accuracy with OCCR and confident OCCR of 100% and 98. 0% for the prediction set, respectively. It was demonstrated that hyperspectral imaging was an effective technology for nondestructive detection of bacterial colonies with high accuracy and high speed. The allocated feature wavelengths by CARS could lay theoretical basis for developing low cost multispectral imaging systems for bacterial colony detection.