Improving the functional annotation of the chicken genome is a key challenge in bridging the gaps between genotype and phenotype. Among all transcribed regions, long non-coding RNAs (lncRNAs) are a major component of the transcriptome and the use of whole transcriptome sequencing (RNA-seq) has greatly improved the identification and characterization of these non-coding genes. In this study, we focused on liver and adipose tissues because of their importance in various economically traits in which energy storage and mobilisation play a roles and also due to the high cell homogeneity of these tissues. We thus investigated 16 RNAseq experiments from each tissue with strand-specific reads for identifying lncRNAs. We automated and implemented a program, called FEELnc, which allowed us to identify around 3,000 chicken lncRNAs longer than 200 bp, multi and mono-exonic and without protein-coding capabilities. FEELnc also classified lncRNAs based on their genomic localizations with respect to the ENSEMBL protein-coding annotation. The intergenic lncRNAs class (~90%) was characterized with respect to the distance and orientation with the closest mRNAs and the intragenic lncRNAs class (~10%) was extracted based on their overlap with mRNAs exons and introns. We then characterized more deeply this repertoire in terms of structure and expression and compare these features with the latest human lncRNA repertoire (Derrien et al 2012). In particular, we identified tissue-specific lncRNAs, and one lncRNA as a good candidate to the regulation of lipid metabolism in liver. This study will further be extended to more species and tissues/cell lines in the context of the FAANG project “Fr-AgENCODE”.