Climate change is a very important issue in the natural sciences, and has received much attention in various research fields. Vegetation phenology may be a good indicator of climate change at the regional or global scale, because of the close relationship between vegetation and climate. In this study, we analyzed the trend of vegetation phenology from 1982 to 2006 and its driving climatic factors in northeastern China, which has experienced a rapid climate change in the past three decades partially due to its high latitude. We used a time series of 15-day averaged NDVI derived from the daily GIMMS AVHRR dataset to analyze the trend of vegetation phenology. We first used a Savitzky-Golay filter to reduce the noise in the NDVI curve to account for data contamination by random factors, then conducted a double logistic fitting to extract phonological parameters. To account for varied phenology responses to climate change among different vegetation types, we analyzed time series of those phonological parameters for the four major vegetation types in northeastern China, including broad-leaved forest, coniferous forest, steppe, and meadow. In addition, we performed a Partial Least Squares (PLS) regression to examine the relationship between vegetation phenology and climatic variables. Results showed that spring phenology exhibited an advancing trend followed by a delay for all four vegetation types, but different vegetation types had different turning points. In contrast, the autumn phenology was somewhat complicated with inconsistent patterns across the four vegetation types. Broad-leaved forest and coniferous forest had an overall delayed trend, but the other two types showed a delay-advancing-delay trend. During the study period of 25 years, the spring phenology advanced 11 days for meadow, 7 days for coniferous forest, 5 days for broad-leaved forest, and 3 days for steppe. Autumn phenology was delayed 6 days for broad-leaved forest, 4 days for coniferous forest, and 1 day for meadow, while the steppe showed an advance of 8 days. Partial Least Squares (PLS) regressions indicated that spring temperature was negatively correlated with the spring phenology of broad-leaved forest, coniferous forest and meadow, while previous year winter temperature was positively correlated with the spring phenology of steppe. The relationship between precipitation and spring phenology was complex without any evident patterns. Except for steppe, the autumn phenology of all vegetation types had a negative correlation with summer precipitation. Spring phenology maybe mainly driven by temperature, while autumn phenology was mainly controlled by precipitation. Our study demonstrated strong effects of rapid climate warming on vegetation phenology in northeastern China, which may exert cascading influences on ecosystem processes and functions such as carbon sequestration and ecosystem productivity.