Where was the TG10N number plate registered?

Vehicles with the TG10N prefix were first registered in the UK through official DVLA regional allocation tag TG.

What year was a TG10N registration plate released?

The TG10N series was issued during March 2010 to August 2010.

What is the MOT pass rate for TG10N vehicles?

Vehicles in the TG10N cohort demonstrate an average historical MOT pass rate of 88.9% based on 9 recorded DVSA inspections.

Batch Prefix Hub

TG10N Series Number Plates

UK vehicles during March 2010 to August 2010 with registration prefix TG10N.

Area Office: DVLA Region (TG)
Release Period: March 2010 to August 2010
Tracked in Batch: 2 vehicles
MOT Pass Rate: 88.9%

Popular Makes

JAGUAR 50% (1)

TOYOTA 50% (1)

88.9%

Average MOT Pass Rate

Based on 9 historical tests

Common Colors

Black1 vehicles

Blue1 vehicles

Active Plates in TG10N Series(2 of 2 displayed)

Filter by 6th Letter:

TG10NEW

MOT Valid

TOYOTA AYGO

1.0L · Petrol · Blue

First Used: Jun 2010
MOT Expiry: 10 May 2027
Tax Status: —
Color: Blue

Check History & Specs

TG10NYG

MOT Valid

JAGUAR XKR

5.0L · Petrol · Black

First Used: Nov 2010
MOT Expiry: 24 Mar 2027
Tax Status: —
Color: Black

Check History & Specs

Reliability & Common Problem Guides for TG10N Makes

Explore common mechanical faults, MOT advisory trends, and recall notices for popular vehicles in this registration batch:

JAGUAR Common Problems TOYOTA Common Problems

Frequently Asked Questions about TG10N Plates

Where was the TG10N number plate series registered?

Vehicles starting with TG10N were assigned to the DVLA regional office covering the UK. The first two letters (TG) identify the memory tag assigned by the DVLA.

What registration period is TG10N?

The TG10N age identifier corresponds to vehicles first registered between March 2010 to August 2010.

How can I check the MOT history of a TG10N vehicle?

Click any registration in the plate list above to view the complete DVSA inspection history, advisory alerts, mileage progression, road tax status, and our AI mechanical risk evaluation.