Where was the UD17N number plate registered?

Vehicles with the UD17N prefix were first registered in the UK through official DVLA regional allocation tag UD.

What year was a UD17N registration plate released?

The UD17N series was issued during March 2017 to August 2017.

What is the MOT pass rate for UD17N vehicles?

Vehicles in the UD17N cohort demonstrate an average historical MOT pass rate of 88.9% based on 9 recorded DVSA inspections.

Batch Prefix Hub

UD17N Series Number Plates

UK vehicles during March 2017 to August 2017 with registration prefix UD17N.

Area Office: DVLA Region (UD)
Release Period: March 2017 to August 2017
Tracked in Batch: 2 vehicles
MOT Pass Rate: 88.9%

Popular Makes

FORD 50% (1)

LAND ROVER 50% (1)

88.9%

Average MOT Pass Rate

Based on 9 historical tests

Common Colors

Grey1 vehicles

White1 vehicles

Active Plates in UD17N Series(2 of 2 displayed)

UD17NNH

MOT Valid

FORD KUGA

2.0L · Diesel · Grey

First Used: May 2018
MOT Expiry: 21 May 2026
Tax Status: —
Color: Grey

Check History & Specs

UD17NNN

MOT Valid

LAND ROVER DISCOVERY SPORT

2.0L · Diesel · White

First Used: Dec 2018
MOT Expiry: 04 Oct 2026
Tax Status: —
Color: White

Check History & Specs

Reliability & Common Problem Guides for UD17N Makes

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

FORD Common Problems LAND ROVER Common Problems

Frequently Asked Questions about UD17N Plates

Where was the UD17N number plate series registered?

Vehicles starting with UD17N were assigned to the DVLA regional office covering the UK. The first two letters (UD) identify the memory tag assigned by the DVLA.

What registration period is UD17N?

The UD17N age identifier corresponds to vehicles first registered between March 2017 to August 2017.

How can I check the MOT history of a UD17N 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.