Foreign objects in vegetables and vegetable products are the primary source of customer complaints. However, there is not adequate research to detect the foreign materials from fresh-cut vegetables. Several studies have proved the necessity of developing an advanced technology to identify the foreign materials from fresh-cut vegetables. The present research has been done to develop a model using near infrared hyperspectral imaging (HSI) techniques to detect a wide range of possible foreign materials mixed with three different types of vegetables: cabbage, green onion, and carrot. Hyperspectral images of fresh-cut vegetables mixed with foreign objects were collected throughout the short wave infrared (SWIR) spectral region (1000 nm to 2500 nm) using a laboratory-based line scan HSI system in reflectance mode. The spectra of each foreign material and vegetable piece were collected and shuffled several times to avoid bias before applying the data to build a partial least square discriminant analysis (PLS-DA) model. 70% of the spectra were used for calibration, and the remaining 30% of data were used to validate the model. The model achieved more than 98% accuracy in both calibration and validation for identifying the foreign objects from all three types of vegetables. The obtained results showed that the near infrared hyperspectral imaging system with multivariate analysis could be an excellent choice for detecting the foreign objects from fresh-cut vegetables except for the black color foreign objects, which were similar in color with the background. This limitation might be solved by incorporating a RGB camera with the near infrared HSI system.