Improved piglet survival during the suckling period is a strong expectation for breeders and the public alike. This notably involves taking into account the maturity of the piglet at birth. An immature piglet, which has not reached its full development, will have a greater risk of early death. These piglets have a characteristic morphology: a domed head, bulging eyes and head/body asymmetry. Based on image analysis, the Pic'Let project (CASDAR-RT 2019) aims to offer breeders an innovative tool for phenotyping piglet maturity. A metabolomics analysis was also performed by 1H-NMR on blood samples (serum) collected on a subset of 298 newborns (99 LR, 98 LW, 98 LRxLW). Raw spectra were analysed with the R package ASICS and 55 metabolites with non-zero variance have been used in following statistics. A first analysis with PCA suggested a common metabolic profile may be identified whatever the genotype. Next, two predictive models based on Random Forest and GLM/Lasso algorithms were developed. Maturity status has two levels: mature (77% of the piglets) vs. light or severe immaturity (23%). The imbalance characteristic of the dataset was adjusted by downsampling and model aggregation. The two models predict 100% of the severe immaturity status in the training and the test samples. Some piglets morphologically determined as mature are expected to be immature with a strong confidence [80-100%] according to metabolic data. Altogether, we identified a molecular signature based on metabolic data able to predict the neonatal maturity status. Next steps will be 1/ to apply this on the complete set of other 600 newborns phenotyping in different context (several genotypes, different farms), 2/ to develop a similar predictive model with available gene expression datasets on same piglets, 3/ to develop similar predictive models on other available traits (mortality, vitality, growth).