Purpose: Spoken language sample analysis (LSA) is widely considered to be a critical component of assessment for child language disorders. It is our best window into a preschool child's everyday expressive communicative skills. However, historically, the process can be cumbersome, and reference values against which LSA findings can be "benchmarked" are based on surprisingly little data. Moreover, current LSA protocols potentially disadvantage speakers of nonmainstream English varieties, such as African American English (AAE), blurring the line between language difference and disorder. Method: We provide a tutorial on the use of free software (Computerized Language Analysis [CLAN]) enabled by the ongoing National Institute on Deafness and Other Communication Disorders-funded "Child Language Assessment Project." CLAN harnesses the advanced computational power of the Child Language Data Exchange System archive (www.childes.talkbank.org), with an aim to develop and test fine-grained and potentially language variety-sensitive benchmarks for a range of LSA measures. Using retrospective analysis of data from AAE-speaking children, we demonstrate how CLAN LSA can facilitate dialect-fair assessment and therapy goal setting. Results: Using data originally collected to norm the Diagnostic Evaluation of Language Variation, we suggest that Developmental Sentence Scoring does not appear to bias against children who speak AAE but does identify children who have language impairment (LI). Other LSA measure scores were depressed in the group of AAE-speaking children with LI but did not consistently differentiate individual children as LI. Furthermore, CLAN software permits rapid, in-depth analysis using Developmental Sentence Scoring and the Index of Productive Syntax that can identify potential intervention targets for children with developmental language disorder.