MIA97LA041
1996-12-10 · PENSACOLA, Florida, United States · None · 1 aircraft · Status: Completed
Airport PNS
Current FAA registration · N6955D
- Make / Model
- PIPER PA-22-150
- Year of manufacture
- 1957 · 39 years old at event
- Engine
- LYCOMING 0-320 SERIES (180 hp)
- Seats / Engines
- 4 seats · 1 engine
- Last airworthiness date
- 19570222
- ADS-B equipped
- Yes — Mode-S A94054
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
failure of the pilot to maintain directional control during the landing roll, which resulted in a ground swerve and subsequent collapse of the nose gear. The crosswind was a related factor.
Factual narrative
On December 10, 1996, about 1125 central standard time, a Piper PA-22-150, N6955D, registered to a private individual, experienced a loss of control during the landing roll at the Pensacola Regional Airport, Pensacola, Florida. Visual meteorological conditions prevailed at the time and a VFR flight plan was filed for the 14 CFR Part 91 personal flight. The airplane was substantially damaged and the private rated pilot, the sole occupant, was not injured. The flight originated about 0835 from the Orlando Executive Airport, Orlando, Florida. The pilot stated that he executed a straight in approach to the runway with a crosswind from the left. After touchdown during the landing roll, the left wing was raised and the right wing tip then contacted the runway. The airplane then yawed to the right collapsing the nose landing gear. The pilot stated that he made a straight-in approach to the runway with a crosswind from the left. After touchdown during the landing roll, the left wing was raised, and the right wing tip then contacted the runway. The airplane then yawed to the right, and the nose gear collapsed. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_1996_MIA97LA041.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
Search this event elsewhere
External sources are reported, not agency: signal that something happened, not fact about what happened.
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Related research
Matched on aircraft type or causal vocabulary (loss of control). All research papers
- 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.
- Semantic Scholar 2024 · Article (International Conference on Networking and Services) Risk Assessment of Loss of Control In-Flight Trajectories for Urban Air Mobility Safety
In Urban Air Mobility (UAM), maintaining a minimum separation between aircraft is a safety requirement, which becomes challenging amidst congested air traffic and off-nominal conditions, such as Loss …
- 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.