Long term global terrestrial vegetation monitoring from satellite Earth Observation system is a critical issue within global climate and earth science modeling applications. This contribution describes the algorithm and a first validation of the global LAI, FAPAR and FCOVER products derived from AVHRR Long Term Data Record (LTDR) for the 1981-2000 period within geoland2 project. GEOV1_AVHRR algorithm aims to ensure robustness and consistency of the derived biophysical products with the ones developed in the recent years, and particularly with the GEOV1_VGT products derived from SPOT/VGT sensor. The approach is based on the use of neural networks and dedicated temporal smoothing and gap filing techniques. The comparison of GEOV1_AVHRR with GEOV1_VGT and with similar products derived from AVHRR GIMMS shows that GEOV1_AVHRR outperforms the current existing products in terms of smoothness and continuity (fraction of valid data of 99.7%). At the same time, GEOV1_AVHRR shows high consistency with GEOV1_VGT and a similar accuracy (RMSE=0.80) as evaluated with ground-based observations. Combination of GEOV1_AVHRR and GEOV1_VGT realizes thus the objective of producing continuous and consistent time series of global observations of LAI, FAPAR and FCOVER for the last three decades. In the next months GEOV1_AVHRR products will be freely accessible at geoland2 portal at 0.05º spatial resolution and 10 days temporal sampling frequency.