Background: Eimeria tenella is an apicomplexan parasite that causes coccidiosis in the domestic fowl. Infection with this parasite is diagnosed frequently in intensively reared poultry and its control is usually accorded a high priority, especially in chickens raised for meat. Prophylactic chemotherapy has been the primary method used for the control of coccidiosis. However, drug efficacy can be compromised by drug-resistant parasites and the lack of new drugs highlights demands for alternative control strategies including vaccination. In the long term, sustainable control of coccidiosis will most likely be achieved through integrated drug and vaccination programmes. Characterisation of the E. tenella transcriptome may provide a better understanding of the biology of the parasite and aid in the development of a more effective control for coccidiosis. Results: More than 15,000 partial sequences were generated from the 5' and 3' ends of clones randomly selected from an E. tenella second generation merozoite full-length cDNA library. Clustering of these sequences produced 1,529 unique transcripts (UTs). Based on the transcript assembly and subsequently primer walking, 433 full-length cDNA sequences were successfully generated. These sequences varied in length, ranging from 441 bp to 3,083 bp, with an average size of 1,647 bp. Simple sequence repeat (SSR) analysis identified CAG as the most abundant trinucleotide motif, while codon usage analysis revealed that the ten most infrequently used codons in E. tenella are UAU, UGU, GUA, CAU, AUA, CGA, UUA, CUA, CGU and AGU. Subsequent analysis of the E. tenella complete coding sequences identified 25 putative secretory and 60 putative surface proteins, all of which are now rational candidates for development as recombinant vaccines or drug targets in the effort to control avian coccidiosis. Conclusions: This paper describes the generation and characterisation of full-length cDNA sequences from E. tenella second generation merozoites and provides new insights into the E. tenella transcriptome. The data generated will be useful for the development and validation of diagnostic and control strategies for coccidiosis and will be of value in annotation of the E. tenella genome sequence.