As one of the most important agricultural resources, the cropland is the basic survival condition for human being. Accurate information on cropland area is of critical importance for assessing food security. The Northeast China includes provinces of Liaoning, Jilin, Heilongjiang and eastern part of Inner Mongolia Autonomous Region. It is one of the most important marketable production bases and output regions with rich water resources, fertile soil and vast cultivated land. With the unprecedented combination of economic and population growth, a dramatic land transformation has caused across this region, and the cropland degradation is increasingly serious. In order to preserve and manage cropland resources, it is essential to investigate and monitor cropland dynamics. Compared to traditional observations in the field, the principal advantage of remote sensing data is the possibility that they offer to gather synoptic information at regular time intervals over large areas. Especially for the muti-temporal images, repeated observations can be used to monitor characteristics of phonological dynamics at regional level. The normalized difference vegetation index (NDVI) which derived from the remote sensed data, is one of the most important parameters for the vegetation growth and was widely used in the land cover classification. In recent years, with the development of the theory about Artificial Neural Network (ANN) system, the neural network technology is becoming increasingly an effective means of classification processing of remote sensor digital images. Therefore, on the basis of the muti-period NDVI, the cropland can be identified and separated from the other land cover types by means of the neural network technology.In this paper, Harmonic Analysis of a Time Series of SPOT/VGT NDVI data was used to develop an innovative technique for cropland identification in Northeast China based on temporal variations of NDVI values during 2007. Different vegetation classes (forest, cropland, grassland, water body) exhibiting distinctive seasonal patterns of NDVI variation have strong periodic characteristics. A Discrete Fourier Filter was applied to NDVI time-series data in order to minimize the influence of high-frequency noise on class assignment. Because of the different phonology and periodicity in various land use types, the amplitude, phase and annual NDVI mean value in the studying area are acquired and integrated for an image. According to the training sample size, the neural network classification measure is introduced to extract cropland. The total accuracy is 83.26% while Kappa coefficient is 0.7324. The accuracy of measurement to extract cropland information is much higher by comparing the four current products (GLC2000 land cover data, UMD land cover data, IGBP land cover data and CAS land cover data). The study indicates that it is feasible for cropland extraction utilization of time-series analysis and neural network classification, and can provide accurate, scientific cropland information for agricultural administrators and land management decision making.