Pesticide residue in leafy vegetables like a cabbage can cause harmful effects to consumers. Thus, early detection and classification of pesticide residue could help consumers to choose residue-free cabbages. This research was performed to evaluate the performance of different classification methods to classify spectral data collected from 60 pesticide-free cabbage samples. Deltamethrin pesticide was sprayed on the samples at different dilution concentrations namely pesticide-free (PF), pesticide-low (PL), pesticide-medium (PM) and pesticide-high (PH). The spectral data of the cabbages was recorded using a spectrometer with an effective wavelength in the range of 400 to 1000 nm. The concentration of the pesticide residues in each cabbage sample was quantified using a gas chromatography with an electron detector (GC-ECD). Three classification methods investigated in this study were artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR). The results show that LR, SVM and ANN yielded excellent classification accuracies of 95, 88 and 87%, respectively. This study revealed that the spectroscopic measurement coupled with classification methods are promising technique for detecting and classifying pesticides residues in cabbage samples.