The rising prevalence of Autism Spectrum Disorder (ASD) in the United States points to an increased need for services across the life span. Specialized services beginning at the earliest age possible are critical to maximizing long-term outcomes for children with ASD and their families. Many children later diagnosed with ASD will begin to receive services through the federally funded Early Intervention (EI) system that serves infants and toddlers from birth to age three. However, without formal recognition, services may not fully address the constellation of ASD symptoms. While ASD training in EI is becoming more widespread, there is still a need for better detection of ASD symptoms at younger ages. We hypothesized that initial EI assessment records which document the strengths and needs of children in EI, could be an important source for detecting ASD warning signs and aid state EI systems in earlier identification. In this research, we used EI records to evaluate classification techniques to identify suspected ASD cases. We improved the performance of machine learning techniques by developing and applying a unified ASD ontology to identify the most relevant features from EI records. The results indicate that using Support Vector Machine (SVM) with ontology-based unigrams as features yields the best performance. Our study shows that developing automatic approaches for quickly and effectively detecting suspected cases of ASD from non-standardized EI records earlier than most ASD cases are typically detected is promising.