NTSB CAROL · Event
Event CHI96LA098
Aircraft involved
Probable cause & findings
Failure of the student pilot to maintain directional control of the airplane, which resulted in an inadvertent ground swerve. A factor relating to the accident was: the crosswind condition.
Factual narrative
On February 24, 1996, at 1425 central standard time, a Cessna 152, N6325Q, operated by St. Charles Flying Service, was substantially damaged following a loss of control and nose over during landing at the Jacksonville Municipal Airport, Jacksonville, Illinois. The 14 CFR Part 91 flight was operating in visual meteorological conditions under a VFR flight plan. The student pilot was not injured and the airplane was substantially damaged. The flight originated from St. Charles, Missouri, at 1326 cst. The student pilot was on his first solo cross country flight when the accident occurred. He stated he aborted his first landing approach because he was not properly aligned with the runway. The pilot stated that during the second landing he was still in a crab angle during the flare. He reported, "...I felt the airplane begin to skid on me as I tried to straighten it out, I panicked as I felt that I lost control of the airplane." The airplane traveled off the side of the runway where it nosed over. The landing was being made on runway 31 (5,000' x 75'). The pilot reported that the local winds were form 210 to 240 degrees at 10 to 15 knots. The student pilot was on his first solo cross-country flight when the accident occurred. A landing was being made on runway 31 with local winds from 210 to 240 degrees at 10 to 15 knots. According to the pilot, the airplane touched down in a crab angle and traveled off the side of the runway where it nosed over in the grass. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_1996_CHI96LA098.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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Related research
What the literature says.
Academic papers and agency reports matching this event's aircraft type or causal vocabulary (loss of control). Sourced from NASA NTRS, NTSB Safety Studies, FAA CAMI, AOPA Air Safety Institute, Embry-Riddle Scholarly Commons, arXiv, and the Semantic Scholar academic graph.
- Embry-Riddle Scholarly Commons 2025 · Journal article (JAAER)
A Scoping Review of Aviation Loss of Control Inflight Research
Loss of control – inflight (LOC-I) contributes to aircraft accidents at unacceptably high rates. Significant industry efforts and research have aimed to improve LOC-I prevention, detection, and recove…
- SKYbrary (Eurocontrol) 2024 · SKYbrary article
Loss of Control In-Flight (LOC-I) — SKYbrary Knowledge Base
SKYbrary comprehensive knowledge-base entry on Loss of Control In-Flight — definitions, contributing factors, accident case studies (Air France 447, Colgan 3407), and prevention strategies.
- NTSB Aircraft Accident Reports 2022 · Accident report
Loss of Control on Takeoff in Icing Conditions — Citation 560XL
Cessna Citation 560XL fatal takeoff icing accident, March 2018. Investigation of a Citation 560XL loss-of-control takeoff accident in icing conditions.
- Semantic Scholar 2021 · Article (Aviation)
ANALYSIS OF GENERAL AVIATION FIXED-WING AIRCRAFT ACCIDENTS INVOLVING INFLIGHT LOSS OF CONTROL USING A STATE-BASED APPROACH
Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source…
- NASA NTRS 2021 · Presentation
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
- NASA NTRS 2021 · Conference Paper
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
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