The physical and chemical heterogeneities of soils make the soil spectral different and complicated, and it is valuable to increase the accuracy of prediction models for soil organic matter(SOM) based on pre-classification. This experiment was conducted under a controllable environment, and different soil samples from northeast of China were measured using ASD2500 hyperspectral instrument. The results showed that there are different reflectances in different soil types. There are statistically significant correlation between SOM and reflectence at 0.05 and 0.01 levels in 550–850 nm, and all soil types get significant at 0.01 level in 650–750 nm. The results indicated that soil types of the northeast can be divided into three categories: The first category shows relatively flat and low reflectance in the entire band; the second shows that the spectral reflectance curve raises fastest in 460–610 nm band, the sharp increase in the slope, but uneven slope changes; the third category slowly uplifts in the visible band, and its slope in the visible band is obviously higher than the first category. Except for the classification by curve shapes of reflectance, principal component analysis is one more effective method to classify soil types. The first principal component includes 62.13–97.19% of spectral information and it mainly relates to the information in 560–600, 630–690 and 690–760 nm. The second mainly represents spectral information in 1 640–1 740, 2 050–2 120 and 2 200–2 300 nm. The samples with high OM are often in the left, and the others with low OM are in the right of the scatter plot(the first principal component is the horizontal axis and the second is the longitudinal axis). Soil types in northeast of China can be classified effectively by those two principles; it is also a valuable reference to other soil in other areas. The physical and chemical heterogeneities of soils make the soil spectral different and complicated, and it is valuable to increase the accuracy of prediction models for soil organic matter (SOM) based on pre-classification. This experiment was conducted under a controllable environment, and different Soil samples from northeast of China were measured using ASD2500 hyperspectral instrument. The results showed that there are different reflectances in different soil types. There are quite significant correlation between SOM and reflectence at 0.05 and 0.01 levels in 550-850 nm, and all soil types get significant at 0.01 level in 650-750 nm. The results indicated that soil types of the northeast can be divided into three categories: The first category shows a flat flat and low reflectance in the entire band; the second shows that the spectral reflectance curve raises the fastest in 460-610 nm band, the sharp increase in the slope, slowly uplifts in the visible band, and its slope in the visible band is obviously higher than the first category. Except for the classification by curve shapes of reflectance, principal component analysis is one more effective method to classify soil types. The first principal component includes 62.13-97.19% of spectral information and it mainly relates to the information in 560-600, 630-690 and 690-760 nm. The second mainly represents spectral information in 1 640-1 740, 2 050-2 120 and 2 200- 2 300 nm. The samples with high OM are often in the left, and the others with low OM are in the right of the scatter plot (the first principal component is the horizontal axis and the second is the longitudinal axis). Soil types in northeast of China can be classified effectively by those two principles; it is also a valuable reference to other soil in other areas.