Background: Given the health, social, and economic burden of neurodegenerative diseases (ND), there is a need for epidemiological studies. Administrative databases, such as the French national health insurance database (SNIIRAM), could offer researchers valuable opportunities. Neurodegenerative diseases can be inferred from data on drug reimbursements, hospital stays, or the registration of chronic conditions. The aim of this study was to describe, in French administrative databases, algorithms used to identify Alzheimer’s disease and associated disorders (ADAD), Parkinson’s disease and associated disorders (PDAD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS). Methods: A systematic literature review was conducted in Medline and gray literature through December 31, 2015. French studies focusing on ADAD, PDAD, MS, or ALS as a primary health outcome, conducted using one of the SNIIRAM data sources (outpatient reimbursements, chronic condition registration, hospital discharge), were included. Results: Thirty-four studies were included (ADAD, n = 18; PDAD, n = 9; MS, n = 4; ALS, n = 3), resulting in 36 algorithms. For each studied ND, there was significant variability in the algorithms regarding (i) the type of criteria used (administrative database versus multi-source systems); (ii) the number of criteria used; (iii) the definition used for each criterion. The extent and level of drug exposure varied widely. Identification through hospitalizations showed variations in terms of type of stay (short stay, long-term stay, psychiatric ward...), the scope of diagnosis codes used, diagnosis type (primary, related, associated diagnosis), and the time period used. A validation study was conducted for 2 of the 36 algorithms (PDAD), and criteria completeness was assessed for 3 algorithms (MS, ALS). Conclusion: despite the increase in the identification of ND in French administrative databases, few algorithms have been validated. Validation studies should be encouraged.