NTSB CAROL · Event
Event DEN00LA004
Registry · N6425X
FAA Aircraft Registry record.
Make / Model
CESSNA 180D
Year of manufacture
1960 · 39 years old at event
Engine
CONT MOTOR O-470 SERIES (230 hp)
Seats / Engines
4 seats · 1 engine
Last airworthiness date
19601114
ADS-B equipped
Yes — Mode-S A86FC6
Registrant of record
GARY EQUIPMENT LEASING LLC
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The excessive taxi speed used by the pilot exiting the runway via a high-speed taxiway resulting in a loss of control. A factor was the crosswind.
Factual narrative
On October 14, 1999, at 1015 mountain daylight time, a Cessna 180D, N6425X, sustained substantial damage when it nosed over during landing roll at Denver International Airport, Denver, Colorado. The private pilot and his pilot certificated passenger were not injured. The flight was operating under Title 14 CFR Part 91 and no flight plan was filed. This personal flight departed Boulder, Colorado, at 0945. Visual meteorological conditions prevailed. According to the pilot, he was exiting the runway (17R) with a right turn onto a high-speed taxiway when the tail and right wing began to rise. The application of heavy breaks had no effect and the aircraft nosed over. At the time of the accident, Denver International Airport recorded wind was from 270 degrees magnetic at 10 knots. There were no gusts recorded. The pilot provided information that he did not believe there was any aircraft malfunction or failure. This was verified by an FAA inspector who examined the aircraft. During landing roll, the aircraft exited the runway via a high-speed taxiway and despite heavy breaking by the pilot the aircraft nosed over. Wind at the time was a direct cross wind recorded at 10 knots. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_1999_DEN00LA004.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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